Systems and methods for evaluation of wine characteristics

ABSTRACT

A method comprises: receiving wine evaluations of wines from wine panelists, each wine evaluation including intensity values describing a plurality of wine characteristics for each of a set of wines, each wine evaluation generated by a wine panelist; generating a global intensity value from particular intensity values describing a particular wine characteristic of a particular wine, the particular intensity values being from the wine evaluations; comparing a selected intensity value generated by a selected wine panelist describing the particular wine characteristic for the particular wine against the global intensity value to determine an accuracy deviation; comparing the accuracy deviation against an accuracy deviation threshold to determine whether the selected intensity value is deemed inaccurate based on the comparison; updating the global intensity value for the particular wine characteristic for the particular wine based on the accuracy determination; and storing the updated global intensity value in a wine database.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of and claims priority to U.S.patent application Ser. No. 15/271,964 filed Sep. 21, 2016, entitled“Systems and Methods for Evaluation of Wine Characteristics,” now U.S.Pat. No. 9,784,722, which is a continuation of and claims priority toU.S. patent application Ser. No. 14/454,689 filed Aug. 7, 2014, entitled“Systems and Methods for Evaluation of Wine Characteristics,” now U.S.Pat. No. 9,494,566, which claims priority to U.S. Provisional PatentApplication Ser. No. 61/863,364 filed Aug. 7, 2013, entitled “Systemsand Methods for Robust Evaluation of Wine Characteristics,” which arehereby incorporated by reference herein. U.S. patent application Ser.No. 13/627,738 filed Mar. 28, 2013, entitled “Systems and Methods forWine Ranking,” and U.S. Provisional Patent Application Ser. No.61/539,937 filed Sep. 26, 2012, entitled “Method and System forHierarchical Wine Ranking,” are also hereby incorporated by referenceherein.

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TECHNICAL FIELD

Embodiments of the present invention(s) relate to wine selections andmore particularly to systems and methods for evaluating winecharacteristics for wine ranking.

DESCRIPTION OF THE RELATED ART

Although the number of mobile and web applications that recommend winesto users based on user-defined categorical requests (e.g., wine type,varietal, region, or food type) is becoming commonplace, theseapplications generally employ one of two inefficient techniques: asimple relational database or crowd sourcing (social networking).Typical relational databases tend to have static (i.e., unchanging)relationships between wines and foods without regard to user-specificpreferences (e.g., based solely on one expert's opinion). For example, atypical relational database approach may always mandate a specific winevarietal for a specific food (e.g., Chardonnay or White Burgundy withchicken in cream sauce). This approach typically ignores the preferencesor palate of the consumer in favor of an expected static interplay ofthe characteristics of the food versus the wine.

While crowd-sourcing techniques try to provide wine recommendationsbased on either personal relationships or statistical or demographicsimilarity between a given user and other users (i.e., the crowd), thesetechniques easily fail to distinguish the popularity of a given winefrom an individual's preferences. In order for crowd-sourced opinion tobe practical, a wine must be “sampled” by many users to providereasonable statistical relationships. For a wine to be sufficientlysampled for crowd-sourcing, the wines must be widely available. Smallerand/or elite wineries are virtually ignored because of the lack ofavailability (e.g., the number of people who have sampled the wines maynot be statistically significant, so these wines are not recommended).

A wine recommendation utilizing crowd-sourcing techniques may have nobasis on the consumer's personal taste or individual preferences.Rather, the wine recommendation is based on the consumer's friends' ordemographic preferences. Unfortunately, the range in wine preferencesbetween users and others' preferences can be significant.

Studies also suggest a negative correlation between expert opinion ofquality and non-expert preferences. It would be helpful to evaluate winecharacteristics in a manner that more accurately accommodates thepreferences of the consumers who actually drink the wine.

SUMMARY OF EMBODIMENTS

In some embodiments, a system comprises a wine panelist interface moduleconfigured to receive wine evaluations of wines from wine panelists,each wine evaluation including intensity values describing a pluralityof wine characteristics for each of a set of wines, each wine evaluationgenerated by a wine panelist; an accuracy management module configuredto generate a global intensity value from particular intensity valuesdescribing a particular wine characteristic of a particular wine, theparticular intensity values being from the wine evaluations, configuredto compare a selected intensity value generated by a selected winepanelist describing the particular wine characteristic for theparticular wine against the global intensity value to determine anaccuracy deviation, and configured to compare the accuracy deviationagainst an accuracy deviation threshold to determine whether theselected intensity value is deemed inaccurate based on the comparison; awine panelist statistical processing module configured to update theglobal intensity value for the particular wine characteristic for theparticular wine based on the accuracy determination; and a wine databaseconfigured to store the updated global intensity value.

A method comprises receiving wine evaluations of wines from winepanelists, each wine evaluation including intensity values describing aplurality of wine characteristics for each of a set of wines, each wineevaluation generated by a wine panelist; generating a global intensityvalue from particular intensity values describing a particular winecharacteristic of a particular wine, the particular intensity valuesbeing from the wine evaluations; comparing a selected intensity valuegenerated by a selected wine panelist describing the particular winecharacteristic for the particular wine against the global intensityvalue to determine an accuracy deviation; comparing the accuracydeviation against an accuracy deviation threshold to determine whetherthe selected intensity value is deemed inaccurate based on thecomparison; updating the global intensity value for the particular winecharacteristic for the particular wine based on the accuracydetermination; and storing the updated global intensity value in a winedatabase.

In some embodiments, a system comprises a wine panelist interface moduleconfigured to receive wine evaluations of wines from wine panelists,each wine evaluation including intensity values describing a pluralityof wine characteristics for each of a set of wines, each wine evaluationgenerated by a wine panelist; a precision management module configuredto receive a first intensity score and a second intensity scoregenerated by a particular wine panelist describing a particular winecharacteristic of a particular wine, the first intensity score and thesecond intensity score being received from a particular wine evaluationof the particular wine panelist, configured to compare the firstintensity value against the second intensity value to determine aprecision deviation, and configured to compare the precision deviationagainst a precision deviation threshold to determine whether theparticular wine panelist is deemed precise as to the particular winecharacteristic of the particular wine based on the comparison; a winepanelist statistical processing module configured to generate a globalintensity value for the particular wine characteristic for theparticular wine based on the precision determination; and a winedatabase configured to store the global intensity value.

In some embodiments, a method comprises receiving wine evaluations ofwines from wine panelists, each wine evaluation including intensityvalues describing a plurality of wine characteristics for each of a setof wines, each wine evaluation generated by a wine panelist; receiving afirst intensity score and a second intensity score generated by aparticular wine panelist describing a particular wine characteristic ofa particular wine, the first intensity score and the second intensityscore being received from a particular wine evaluation of the particularwine panelist; comparing the first intensity value against the secondintensity value to determine a precision deviation; comparing theprecision deviation against a precision deviation threshold to determinewhether the particular wine panelist is deemed precise as to theparticular wine characteristic of the particular wine based on thecomparison; generating a global intensity value for the particular winecharacteristic for the particular wine based on the precisiondetermination; and storing the global intensity value in a winedatabase.

In some embodiments, a system comprises a wine panelist interface moduleconfigured to receive wine evaluations of wines from wine panelists,each wine evaluation including intensity values describing a pluralityof wine characteristics for each of a set of wines, each wine evaluationgenerated by a wine panelist; a bias management module configured togenerate a global intensity value from particular intensity valuesdescribing a particular wine characteristic for each of the set ofwines, the particular intensity values being from the wine evaluations,configured to compare respectively selected intensity values generatedby a selected wine panelist describing the particular winecharacteristic across the set of wines against the global intensityvalues across the set of wines to determine accuracy deviations acrossthe set of wines, configured to modify the particular intensity valuesif the accuracy deviations across the set of wines meet a predeterminedcondition; and a wine panelist statistical processing module configuredto update the global intensity values describing the particular winecharacteristic for the set of wines based on the accuracy deviations;and a wine database configured to store the updated global intensityvalues.

In some embodiments, a method comprises receiving wine evaluations ofwines from wine panelists, each wine evaluation including intensityvalues describing a plurality of wine characteristics for each of a setof wines, each wine evaluation generated by a wine panelist; generatinga global intensity value from particular intensity values describing aparticular wine characteristic for each of the set of wines, theparticular intensity values being from the wine evaluations; comparingrespectively selected intensity values generated by a selected winepanelist describing the particular wine characteristic across the set ofwines against the global intensity values across the set of wines todetermine accuracy deviations across the set of wines; modifying theparticular intensity values if the accuracy deviations across the set ofwines meet a predetermined condition; updating the global intensityvalues describing the particular wine characteristic for the set ofwines based on the accuracy deviations; and storing the updated globalintensity values in a wine database.

Other features and aspects of various embodiments will become apparentfrom the following detailed description, taken in conjunction with theaccompanying drawings, which illustrate, by way of example, the featuresof the various embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts two digital devices in communication with a wine rankingsystem over a communication network, according to some embodiments.

FIG. 2 depicts a block diagram of a wine ranking system, according tosome embodiments.

FIG. 3 depicts an exemplary abbreviated wine database entry, accordingto some embodiments.

FIG. 4 depicts a block diagram of a digital device, according to someembodiments.

FIG. 5 depicts a flowchart of a method for generating a wine database,according to some embodiments.

FIG. 6 depicts a flowchart of a method for training a user database,according to some embodiments.

FIG. 7 depicts a flowchart of a method for a user receiving ranked wineson the user's digital device, according to some embodiments.

FIG. 8 depicts a flowchart of a method for providing ranked wines to theuser's digital device in response to a wine request, according to someembodiments.

FIG. 9 depicts a flowchart of a method for updating a user winedatabase, according to some embodiments.

FIG. 10 depicts a block diagram of an exemplary digital device,according to some embodiments.

FIG. 11 depicts an environment that facilitates identifying propertiesof wines and matching wines with the preferences of users.

FIG. 12 depicts a block diagram of a wine panelist data processingsystem, according to some embodiments.

FIG. 13 depicts a flowchart of a method for processing wine rankingsusing the accuracy of an evaluation of wines from a wine panelist,according to some embodiments.

FIG. 14 depicts a flowchart of a method for processing wine rankingsusing precision of an evaluation of wines from a wine panelist,according to some embodiments.

FIG. 15 depicts a flowchart of a method for processing wine rankingsusing an identified bias on the part of a wine panelist, according tosome embodiments.

FIG. 16 depicts a table that illustrates the difference betweenintensity values of two wine panels, according to some embodiments.

FIG. 17 depicts a table that illustrates the difference betweenintensity values of two wine panels, according to some embodiments.

FIG. 18 depicts a table that illustrates the difference betweenintensity values of two wine panels, according to some embodiments.

FIG. 19 depicts a table that illustrates the difference betweenintensity values of two wine panels, according to some embodiments.

FIG. 20 depicts a table that illustrates the difference between thescores by the number of wines per flight, according to some embodiments.

FIG. 21 depicts a table that illustrates the difference between thescores by the number of wines per flight, according to some embodiments.

FIG. 22 depicts a table that illustrates average panelist bias,according to some embodiments.

FIG. 23 depicts a table that illustrates the results of wine rankingprocessing for several varieties of wine, according to some embodiments.

FIG. 24 depicts a table that illustrates principal component analysis ofa dataset of descriptors, according to some embodiments.

FIG. 25 depicts a table that illustrates principal component analysis ofa dataset that excludes non-repeatable descriptors, according to someembodiments.

FIG. 26 depicts a table that illustrates principal component analysis ofa dataset that excludes non-repeatable descriptors and outliers,according to some embodiments.

FIG. 27 depicts a table that illustrates principal component analysis ofa dataset that excludes outliers, according to some embodiments.

FIG. 28 depicts a table that illustrates repeatability analysis of adataset of wines, according to some embodiments.

FIG. 29A depicts a diagrams that illustrates a cluster dendogram andmultidimensional scaling of panelist groupings, according to someembodiments.

FIG. 29B depicts a diagrams that illustrates a cluster dendogram andmultidimensional scaling of panelist groupings, according to someembodiments.

FIG. 29C depicts a diagrams that illustrates a cluster dendogram andmultidimensional scaling of panelist groupings, according to someembodiments.

FIG. 29D depicts a diagrams that illustrates a cluster dendogram andmultidimensional scaling of panelist groupings, according to someembodiments.

FIG. 30A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 30B depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 30C depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 31A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 31B depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 31C depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 32A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 32B depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 32C depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 33A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 33B depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 33C depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 34A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 34B depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 34C depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 35A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 35B depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 35C depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 36A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 36B depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 36C depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 37A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 37B depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 37C depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 38A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 38B depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 38C depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments.

FIG. 39 depicts an example of a wine score sheet for sensory evaluation,according to some embodiments.

FIG. 40 depicts an example of a workflow for preparing wine panelistsfor tasting wines, according to some embodiments.

FIG. 41 depicts an example of multidimensional scaling of results fromwine panelists, according to some embodiments.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

In various embodiments, user-specific input, experiences, and feedbackmay be used to train a system to rank wines from a general winedatabase. The system may provide wine rankings in the context of userpreferences without regard to other users or static relationships. Oneexemplary method uses numerical (or other scoring criteria) winecharacteristics (e.g., based on a predefined character “map” and anexpert-derived intensity scale), which are compiled for wines from auser-specific wine experience database to compute a statistical “proxy”of the user's experiences and preferences in wine characteristics. This“wine proxy” may be treated as a linear mathematical operator by whichfuture user wine requests or searches can be filtered in order toprovide user-specific ranked results for the purposes of purchase orgeneral wine education. Once users have tried wines from the system'sranked wines, they can provide their own ratings which are incorporated(e.g., via a proxy regression) into future rankings from the database.

Some embodiments allow a relatively small wine database to be used togenerate useable statistics thereby making the system efficient. Theuser proxy and subsequent filtering operations may be generated withlittle computational overhead (i.e., the system may be scalable). Thoseskilled in the art will appreciate that, in some embodiments, the winecharacteristics used for analysis may be uncorrelated and/or may bestatistically independent.

As opposed to other techniques known in the fields of machine learningsuch as Bayesian classification (based on poorly informed priorprobabilities or poor assumptions of parameter independence), or clusteranalysis (which usually depends on vague distance measures in aparameter space), some exemplary embodiments may: 1) assure thatparameters are uncorrelated (and usually independent in cases ofGaussian posterior probability distributions); 2) be implemented so asto be insensitive to statistical priors; and 3) maintain wine charactercorrelation information for future classifications or rankings. Inaddition, the analysis may be performed in a reduced-dimensional userpreference space, which may add efficiency to the problem of statisticalclassification with large datasets.

The following example method can have several embodiments including, butnot limited to, web-based or mobile application, staticplatform-specific application (e.g., PC/MAC/Linux), or cloud-based(server-based) application.

FIG. 1 depicts two digital devices 102 and 104 in communication with awine ranking system 108 over a communication network 106 in someembodiments. The digital device 102, digital device 104, and the wineranking system 108 may be digital devices. A digital device is anydevice with memory and a processor. In some examples, digital devices102 and 104 may be a mobile or stationary user device such as, but arenot limited to, smart phones, cell phones, laptops, media tablets,desktop computers, ultrabooks, smart peripherals (e.g., smart glasses),media players, or the like. In some embodiments, the digital device 102and/or digital device 104 may comprise an application (e.g., an app)that communicates with the wine ranking system 108.

In various embodiments, a user of either digital device 102 and 104 mayregister with and/or request wine rankings from the wine ranking system108. In one example, digital device 102 provides the wine ranking system108 with information regarding a user's wine preferences. The wineranking system 108 may build a user wine database based on the user'spast wine consumption and indications of the user's preference for thewine. The wine ranking system 108 may determine the user's preferencesof different wine characteristics (e.g., acidity, sugar, alcohol, andtannins) and then rank a list of wines based on the user's personallydesired characteristics.

The wine ranking system 108 may comprise a wine database. The winedatabase may comprise wine identifiers as well as wine descriptorscorrelated with each wine identifier. A wine identifier identifies aparticular wine (e.g., Robert Foley Claret 2010). A wine descriptor is acharacteristic of a wine (e.g., acidity, alcohol, sugar, tannins, or thelike). Each wine descriptor may have an associated intensity value. Anintensity value represents a degree of actual and/or perceived presenceof the wine characteristic. An intensity value may be defined for acertain range. For example, an intensity value may be zero to six, withzero indicating that the related wine characteristic is not present(e.g., no perceivable tannins) and a six being a maximum amount of therelated wine characteristic. Those skilled in the art will appreciatethat there may be any range or representation of intensity values.

The wine ranking system 108 may utilize the user wine database to selectwines based on a similarity of the user wine characteristic preferenceswith the different intensity values of descriptors of wines contained inthe wine database.

In one example, the user may register with the wine ranking system 108and train a user wine database based on previously preferred wines andthe user's past experiences. The user may train and/or update anassociated database by providing wine identifiers (e.g., brand names,varietals, and/or vintages) as well as an indication of how much theyenjoyed the wine (e.g., one to five stars).

After the user wine database is created and trained, the digital device102 may provide a wine request to the wine ranking system 108 to requesta selection of wines as well as a ranking of wines selected. The winerequest may identify the user (and/or the digital device 102) and mayprovide one or more categorical classifications. A categoricalclassification is a category associated with wine such as, but notlimited to, a name of a wine, a varietal of a preferred type of wine, aregion of preferred wines, a color of preferred wines, and/or the like.The wine database may associate one or more wines and/or intensityvalues with categorical classifications. In various embodiments, theuser may not be sophisticated and, as a result, the user may providegeneral types of wine or categorical classifications.

The wine ranking system 108 may generate a wine proxy for the user basedon the user's past experiences with wine and preferences. The wine proxymay be utilized to select and rank a list of wines based on the winedatabase. For example, the wine ranking system 108 may correlate or findsimilarities of the user wine proxy to the predetermined descriptorsand/or intensity values of different wines contained within the winedatabase. The system may utilize these similarities or correlations toselect and/or rank wines for the user. The wine ranking system 108 mayalso utilize one or more categorical classifications from a wine requestand the user's wine proxy to select, rank, and/or filter a list ofselected wines as further described herein. The ranked wines may beprovided to the user via the requesting digital device 102.

In various embodiments, the wine ranking system 108 may comprise or beassociated with a web server that provides wine recommendations and/orrankings to the digital device 102 via the Internet. The wine rankingsystem 108 may include any number of digital devices (e.g., servers)configured to identify and/or rank wines for users.

Those skilled in the art will appreciate that the wine database and/oruser wine databases may be stored on any digital device such as thedigital devices 102 or 104 or the wine ranking system 108. Further, thewine database and user database may be stored on one or more otherdatabases in one or more other digital devices coupled to thecommunications network 106, the digital device 102, the digital device104, or the wine ranking system 108.

Although only two digital devices are depicted in FIG. 1, those skilledin the art will appreciate that there may be any number of users withuser databases and/or associated digital devices. Further, there may beany number of networks 106 and/or wine ranking systems 108. In someembodiments, the wine ranking system 108 recommends wine based on theuser's preferences as described herein. Those skilled in the art willappreciate that the wine ranking system 108 may operate to recommendwines, rank wines, or both.

FIG. 2 is a block diagram of a wine ranking system 108 in someembodiments. The wine ranking system 108 may comprise a wine descriptionmodule 202, a training module 204, a ranking module 206, an updatemodule 208, a registration module 210, a user profile database 212, anda wine database 214. The wine description module 202 may generate and/orupdate a wine database. The wine database is a database or any datastructure that comprises wine identifiers with corresponding winecharacteristics. Each discrete characteristic for a wine, or descriptor,may be based, for example, on aroma or taste. A wine descriptor is awine characteristic such as, but not limited to, acidity, alcohol,sugar, tannins, or the like which may be used to describe any number ofwines. These discrete characteristics, or descriptors, for each wine maybe elementized (i.e., identified), and quantified (i.e., intensitiesassigned) to create a discrete parameter. Each wine descriptor may havean associated intensity value. An intensity value may be any value suchas a number or score that represents a degree of actual and/or perceivedpresence of the descriptor in a particular wine.

In some embodiments, one or more wines in the wine database may beassociated with any number of categorical classifications (e.g., winename, varietal, vintage, region, and/or appellation). Categoricalclassifications include categories that may apply to and classify wine.The categorical classifications may include categories that relate towine that may represent a type of wine (e.g., color, varietal, vintage,region, winery, wine name, appellation, or the like).

The wine database may comprise information regarding wines numbering asfew as hundreds to as many as millions of distinct wines (constitutingan appropriate representation spanning various wine types, varietals,regions, etc.).

In some embodiments, experienced individuals (e.g., experts) identifywines and quantify a set of discrete parameters. The experiencedindividuals may be any individual with training and/or experience todescribe wine utilizing the discrete parameter set. Wines that define aset of discrete data may be analyzed by the experienced individuals interms of the wine characteristics or parameter set. An exemplary methoddefines wine characters using wine tasting criteria and descriptivelanguage.

Experts or any individuals may be trained to utilize a scoring system(e.g., determine intensity values) for a limited number of winecharacteristics (e.g., descriptors) of the discrete set. By training theindividuals to use the scoring system and the previously determineddescriptors, different experts may utilize a similar language (e.g.,based on the descriptors) and a more objective approach to describingwine.

In some embodiments, each experienced individual may assign numericalvalues (i.e., scores or intensity values) denoting how much a particularcharacter is perceived to be represented in each wine. The intensitiesmay be assigned utilizing a taster-subjective (i.e., expert opinion)intensity scale. The intensity value may be based on or converted to anyscale. In one example, the intensity values may range from 0-6 for eachcharacter (i.e., for each descriptor). Those skilled in the art willappreciate that any range of intensity values may be utilized. Theintensity values need not be restricted to integers. The intensityvalues may be positive, negative, or a combination of both.

The character data, the relative intensities, and the wine descriptiveinformation constitute a wine parameter database that may cover manydifferent wine varietals, geographic regions, wine producers, andvintages.

FIG. 3 depicts an exemplary abbreviated wine database entry in someembodiments. In this case, the first six columns correspond to variousidentifying and searchable information for the wine, while the last fourcolumns represent example wine characteristics and their relativeintensities assigned by the system. Here, the wine has characteristicsdenoting “Fruit, Earth, Spice, and Resin” with respective intensitiesequal to “0.5, 2.5, 2.0 and 3.5,” respectively. This wine with its fourparameters and intensities would then represent one datum for a4-parameter character set. Those skilled in the art will appreciate thatthe number of characters may be much larger than four and the number ofwine entries much larger than one.

Those skilled in the art will appreciate that any group of individualsand/or analytical devices may be used to associate wine with differentdescriptors and intensity values. An individual may not need to be arecognized expert to be able to associate the wine with a descriptor andintensity value. For example, a set of individuals (e.g., students) maybe trained to utilize the system to associate different wines withdescriptors and intensity values. The information may be stored in thewine database.

In various embodiments, natural language processing (NLP) techniquessuch as machine learning may be used to interpret contextual semantictext from existing wine tasting notes. For example, wine tasting notesor wine reviews from any expert or other individual may be processed toidentify descriptors and intensity values associated with thosedescriptors. Natural language processing may be utilized with anydocument describing wine, including documents such as web pages orportions of web pages available on the Internet. Other sources mayinclude wine bottle labels, wine descriptions in magazines or tradepublications, blogs, Facebook discussions, or the like.

Natural language processing may scan and convert language to descriptorsand intensity values. See Table 1 for an example of a very simplesemantics-based character intensity scale.

TABLE 1 Example of semantic description and corresponding intensitiesfor relative wine character scale. Suggested Scale Words typical oflevel of intensity Value “aromas, nose of” 0.5 (MINIMUM)  “nuance, hint,pungent-nose” 1.0 “mild, little, bit, light, touch” 1.5 “some, notes of”2.0 If character is mentioned with NO descriptor 2.5 “(spice)‘y’,(fruit)‘y’, etc.” 3.0 “plenty, long, moderate, layered, concentrated”3.5 “lingering, pungent, powerful, generous” 4.0 “extravagance,abundance, intense, over-powering” 4.5 “lots, burning, excessive” 5.0(MAXIMUM)

Wine identifiers (i.e., identifiers that identify a specific wine),associated descriptors and related intensity scores (e.g., by utilizingexperts and/or natural language processing) may be stored in the winedatabase.

The training module 204 of FIG. 2 is configured to generate and/or traina user wine database based on wines identified by the user. In variousembodiments, in order to train the system to rank wines for each user, auser database of wines (e.g., a subset of the wine database) that isunique to each user is determined from input about the user's wineexperiences. In one example, a user input may comprise messages from theuser regarding wines, preference scores, and/or any other information.

In some embodiments, to keep the input simple, the system need notrequire the user to input specific characteristics in wine they like(since the user may or may not be knowledgeable of winecharacteristics). Rather, the system (e.g., training module 204) may askthe user and/or the user may provide examples of wines or wine typesthat they have experienced and prefer. An exemplary system query for theuser may request general wine information related to wines preferred orconsumed in the past (See Columns 1-6 in FIG. 3). Such categoricalclassifications may include, but are not limited to: user preference forwine type(s), region(s), varietal(s), or producer(s). This informationmay then be used to limit (i.e., match) the wines in the wine databaseto a subset of wines (i.e., the “training” database) unique to eachuser's experiences.

In various embodiments, the training module 204 may translate one ormore categorical classifications to wine descriptors and/or userpreference intensity values. For example, the user may be provided alimited list of categorical classifications to choose from. Theselections may be provided to the training module 204 which mayassociate the categorical classifications with one or more descriptors.The user preference intensity values related to the categoricalclassifications may be determined. A user preference intensity value isan intensity value associated with a descriptor. The intensity value maybe provided by the user or determined based on information provided bythe user. If the categorical classification does not relate to anintensity of the descriptor, the user preference intensity valueassociated with the categorical classification may be given a neutralvalue.

For example, the user may provide the wine ranking system 108 one ormore categorical classifications. The training module 204 may retrievewines from the wine database that match the one or more categoricalclassifications. The intensity scores of descriptors associated with theselected wines may be utilized to generate (e.g., by averaging thepreexisting intensity scores) user preference intensity values. In someembodiments, the user may provide additional information, such as winepreference and appreciation value which may be used to weigh anddetermine the user preference intensity values. For example, the usermay provide a categorical classification indicating that the userprefers red wine. Descriptors and related intensity values of winesassociated with red wines may be utilized to generate the user's wineproxy (further discussed herein).

The training module 204 may also determine when limited user input issufficient for further analysis based on statistical and hypothesistests for small sample populations (e.g., critical T-values). Theresulting training database may be used to calculate user-specificstatistics. The user database may be stored and updated as necessary bythe system for future use. Those skilled in the art will appreciate thatany number of wines may be sufficient.

The ranking module 206 is configured to identify wines of interest tousers based on descriptors and user preference intensity values from theassociated user database (e.g., user profile). In various embodiments,the ranking module 206 takes parametric information (characters andintensities) from the user training database, performs a spatialcorrelation across parameters and wine entries, and uses the resultingstatistical correlations to mathematically reduce the parameter set to alimited number of new uncorrelated variables which, taken in linearcombination, may uniquely define the user's wine preference (i.e., theuser wine proxy). There are multiple embodiments of this statisticalde-correlation process which may include, but not limited to: principalcomponent analysis (PCA), independent component analysis (ICA), singularvalue decomposition (SVD), or other matrix de-correlation method (e.g.,discrete cosine transform (DCT), wavelet transform, or orthogonalpolynomial decomposition).

Describing an approach utilizing principal component analysis as oneexample, the mathematical procedure transforms, a number of correlatedvariables (i.e., wine characters in this case) into an equal number ofuncorrelated variables (vectors), called principal components, whilemaintaining their full variance and ordering the components by theircontribution. The resulting transformation may be such that the firstprincipal component represents the largest amount of variability (i.e.,has the largest weight), while each successive component may account forat least some of the remaining variability. The number of wineparameters can be reduced significantly by replacing them with the firstfew principal components (based on relative amplitudes of weights) thatcapture most of the wine character variance.

In one example, let us assume that the training database has M wines andthat each wine has N characters (e.g., descriptors). A wine charactercovariance (N×N) matrix then can be estimated from the training databaseaccording to the approximation:

$\begin{matrix}{C = {{1/M}{\sum\limits_{i = 1}^{M}{\left\lbrack {{ch}_{i} - {\langle{ch}\rangle}} \right\rbrack^{T}\;\left\lbrack {{ch}_{i} - {\langle{ch}\rangle}} \right\rbrack}}}} & (1)\end{matrix}$

where M is the total number of wines in the training database,

ch

is the wine mean character intensity vector computed for all Ncharacters (i.e., intensity values) across all M wines, and ch_(i) iseach character intensity vector (length=N) for each of the M wines indatabase.

The covariance matrix may include all or some wines that are included inthe wine database. The covariance matrix, which may be termed an“experimental covariance,” may be an estimate of the true covariance ofall wines (including those that are not in the database). The covariancemay be estimated (i.e., the estimated or experimental covariance). Everycovariance may be centered.

In some embodiments, the statistics across all wines and all descriptorsare averaged to get the mean which is subtracted from all wine in thewine database. In one example, an average of all descriptors across allwines is taken (e.g., average vector) and subtracted. The summation isthe residuals (what is left) for all descriptors of all wines. Thecovariance may be computed by taking the correlation between everydescriptor and every other descriptor. In some embodiments, the processcorrelates and/or relates descriptors with each other (e.g., how sugarrelates to color, how sugar relates to alcohol, and the like).

For a twenty-seven (27) descriptors matrix, the covariance should be27×27 (e.g., 27 squared values including a correlation betweenthemselves and every other descriptor). The summation is the correlationbetween every descriptor in the database. Since C is a symmetricsemi-positive definite matrix, the principal components of the trainingdatabase may be computed by solving what is known as the Eigenvalueproblem for the N wine characters:

Cλ=λV  (2)

The matrix V contains the N Eigenvectors (i.e., principal components) ofthe de-correlated user wine parameter basis. The vector λ contains the NEigenvalues (principal component weights representing the relativeimportance of each individual Eigen-character V_(i), in describing theuser's wine “type”).

In various embodiments, the ranking module 206 may pick a small orsmallest subset, P<<N, of Eigenvectors (e.g., in order to reduceambiguity or uncorrelated noise in our character space) from this basethat adequately account for most of our wine character variabilityaccording to the criteria, e.g.:

Σλ_(i)[1:P]/trace[C]≧70%.  (3)

Trace of C may be the summation of diagonal terms in a matrix. Weightsfor all columns of matrix V may be decreasing from largest to smallest.As a result, the first principal component of V first column may have alarge λ compared to all the others. Although 70% represents the numberof λ values whose sum is approximately 70% of the summation of all λvalues, equation (3) may be to any percentage (e.g., higher or lowerthan 70%). In various embodiments, by cutting off λ values, noise may bereduced. Further, as the percentage is decreased, the process may becomemore efficient. In some examples P<10.

Whether the system uses all N or just P components of the de-correlatedbasis, these new wine Eigen-characters approximate the variance (and toa lesser extent the correlation) of wine characteristics (about the mean“composite wine” vector

ch

) in each user's wine database (e.g., user profile) following themathematical form:

var[wine]_(user) =

ch

+λ _(user) V.  (4)

We have denoted the user's variance in preferred wine experiences asvar[wine]_(user). In this context, the larger each λ_(i), the moreimportant (and more correlated across the database) each component,V_(i), may be in describing the likes of the user for the particular setof wines in the training database; equation 4 may completely describethe user's individual “wine proxy” as the set of Eigen-characters V(i.e., a linear mathematical filter), the mean character vector

ch

and the relative importance values λ_(user) (i.e., the filter weights).All or some of the components of the user proxy may be stored by thesystem for future steps.

In essence, equation 4 may project the wine characters into a newmathematical space (i.e., the user “proxy space”) that exploits thestatistical relationship between different wine characters. This isuseful, because it: 1) allows the system to define wines with fewervariables (since, for example, “acidity” and “resin” winecharacteristics may be perfectly correlated in many wines, we canrepresent both with a single principal component rather than the morecomplicated individual characteristics), and 2) it provides the systemwith a filter to make certain all future user wine requests and rankingsare statistically consistent with each user's prior experiences.

Those skilled in the art will appreciate that the largest Eigenvalue(λ₁) in equation 4 may represent the least distinguishing proxycharacter for wine, because all wine share this character (this v₁represents the maximum correlation between all wines in the subset),while the smallest Eigenvalue (λ_(N)) may represent the mostdistinguishing proxy character, because it is correlated between winesless than all other wine characters—it may be the most uniqueEigen-character.

In some embodiments, matrix V is consistent with equation 2 and specificto the user. The statistical proxy may include the user's λ values, V,and CH. The ranking module 206 may utilize this process to create abasis for initial ranking of wine.

Once the user proxy is computed, future user wine requests may befiltered by the operator V in order to transform all wines from a new“dynamic” database into the user's proxy space. To this end, equation 4may allow the user to specify new wine descriptors (e.g., wine type,varietal, producer, region) they are currently interested in having thesystem rank. The update module 208 then uses this information to build adynamic database which is distinct from the training discussed herein.In one example, the update module 208 updates the existing user databasewith wines to those of current interest. Then the update module 208“projects” each wine (e.g., the update module 208 projects each wine'scharacteristics as defined herein) contained in this dynamic database tothe user proxy space by solving the small (P×P) principal component (PC)problem:

λ_(wine) _(i) =[ch _(wine) _(i) −

ch

]V  (5)

Here, λ_(wine) is each of i wines PC defined by each character vector,ch_(wine), contained in the dynamic database and filtered by theEigen-vector operator V.

The system may rank (in either ascending or descending order) all iwines from the dynamic database according to their mathematicalsimilarity/difference, S_(i), in the proxy space to the previouslydefined user wine proxy values, λ_(user), from equation 4:

$\begin{matrix}{S_{i} = {\sum\limits_{i = 1}^{P}{{\lambda_{user} - \lambda_{{wine}_{i}}}}}} & (6)\end{matrix}$

This similarity/difference values can also be determined using anynumber of techniques including Euclidean norm (simple summed differenceas shown in equation 6), mean difference, root-mean-squared difference,chi-squared, etc.

In various embodiments, every wine in a database that matches a searchmay be assessed. In one example, wines are retrieved that match a searchbased on a user wine request and then the related descriptors may beconverted to a mathematical space to look for similarity with thestatistical proxy.

In various embodiments, the term ranking may include that the systemincludes all matching wines from the dynamic database, but then ranksthem according to similarity, S, to user likes/dislikes (e.g.,correlating the preexisting intensity values stored in the wine databasewith the user preference intensity values of the user wine database.Then the user can choose any of the wines they want according to orregardless of ranking. That is, the system may return all wines withadditional information, but allows the user to decide on the wine. Theterm recommendation may include that the returns either a very limitedsubset of all matching wines from the dynamic database for the user tothen choose from or the system returns all wines that match search termsirrespective of degree of similarity, S. Here, the system does most ofthe choosing for the user.

Because the system has statistical information regarding the user's winepreferences, the system may rank wines that are not in the dynamicdatabase from Step 3. For example, if the composite wine,

ch

, from equation 4, is more similar to the user's proxy than any othersingle wine from the dynamic database. In this sense, the exemplarysystem may interpret the mean wine vector to be a new composite wine(one derived from statistics not from the database of wines) that itselfcan be matched against the complete Step 1 static database, per equation6, to compute a new ranking for all other wines outside the dynamicdatabase. This may allow the system to provide the user with a set ofalternative wines that potentially fit their taste better than anysingle wine from the types, producers, regions, etc. that theyrequested.

In one example, the user wine database is trained to include informationregarding a preference for light-bodied wines from the south of France.Subsequently, the user may request that the system rank Barolos fromItaly and Napa Valley cabernets. In computing the mean composite wine,

ch

, from equation 4, the system may determine that the statistical meanfits the user's proxy (according to equation 6) better than anyindividual Barolo or Napa Valley cabernet. The system then may thenmatch the composite to the entire static database and rank all winesrelative to the composite. In this sense, the system may triangulate torank wines that may be preferred by the user more than (but are stillconsistent with) either Barolos or Napa Valley cabernets.

In some embodiments, the system stores at least some wine rankings fromSteps 2 and 4 that the user specifies and allows the user to rate (e.g.,on a relative scale from 0-5) the wines they have tried from this listover time. The user wine proxy may then be updated to reflect these userfeedback ratings by solving a regression problem (mathematical fittingproblem). This technique (which has many embodiments) may incorporatenew observations (user ratings) into the user proxy vector (λ) via thegeneral mathematical form:

[λ_(update) ]=[λR _(W)λ^(T) +εI] ⁻¹λ^(T) R _(W) C  (7)

R_(W) is a diagonal weighting matrix containing the relative userratings for each wine, I is identity matrix, ε is a damping term forstabilization, C is the vector containing the sum of each wine vectorresidual (projected into the proxy space) for all wines (stored by thesystem from the previous training and ranking steps), and λ is perequation 5 for each wine. This updated λ_(update) is used to updateλ_(user) (λ_(new)=λ_(update)·λ_(update)), is stored by the system, andreplaces λ_(user) in all future Step 4 rankings. The average

ch

is also updated accordingly from the composite list of all wines ratedand in the dynamic database. Then as the user tries/rates more wines,the system will better adapt to the user's likes/dislikes and rankingswill increase in accuracy going forward.

Retrieved (e.g., selected) wines may be ranked based on the similarityto the statistical proxy. The identifiers (e.g., labels, names, or thelike) of the wines may be ranked. In some embodiments, when the rankedwines are provided, wine identifiers, location where the wine isavailable, degree of similarity, and/or pricing may be provided to theuser. In some embodiments, the ranking module 206 may provide a valuenumber based on price and fitness (e.g., akin to a PE ratio of a stock).

In various embodiments, the wine ranking system 108 provides a rankingof wines based on a subset of the wines in the wine database. Forexample, the user may request wines that are available based on location(e.g., restaurant, wine bar, or the like) and/or based on categoricalclassifications (e.g., wine color, winery, or the like). In someembodiments, the wine ranking system 108 may select a subset of the winedatabase to correlate with the user's wine proxy.

In one example, the selected subset of the wine database may includewines that are available at the user's location (e.g., wines of ForbesMill Steakhouse of Los Gatos, Calif.) but not include wines that are notavailable at the user's location. Similarly, the wine ranking system 108may select a subset of wines from the wine database based on categoricalclassifications. For example, the user may request a selection and/orranking of wines that are red in color and is described as having alight body. The wine ranking system 108 may receive the categoricalclassifications in a wine request and select a subset of wines from thewine database that meet the categorical classifications.

In some embodiments, the wine ranking system 108 may utilize all winesin the wine database but subsequently select a subset of ranked and/orindividually identified wines based on the user's location and/orcategorical classifications. For example, all wine of the wine databasemay be ranked based on the user's wine proxy. The results may befiltered based on the user's location (e.g., only wines currentlyavailable at Beltramos Wine and Spirits or Beverages and More!) or basedon the categorical classification(s) (e.g., red wines from Paso Robles,Calif.). The subset or filtered results may be provided to the user.Those skilled in the art will appreciate that there are many ways toidentify and/or rank one or more wines based on the wine request.

The update module 208 is configured to update the user database (i.e.,the user profile). In various embodiments, the update module 208 mayreceive an update request from a user via a digital device 102. Theupdate request may include an identifier (e.g., a user or digital deviceidentifier), a wine identifier as well as an indication of preference(e.g., 1-5 stars). The update module 208 may update the user's proxybased on the new information. For example, the update module 208 mayretrieve a wine from the wine database based on the wine identified inthe update request, weigh preexisting intensity values from the winedatabase based on the indication of preference and recalculate theuser's wine proxy including the new information.

The registration module 210 is configured to register users. In variousembodiments, the digital device 102 may provide the wine ranking system108 a wine registration message. The wine registration message mayinclude a user identifier (e.g., username, password, and the like) aswell as other information personal to the user. In one example, the wineranking system 108 comprises a web page that requests registrationinformation (e.g., user identifier and other information) from aninterested user. The registration module 210 may receive the informationand generate a consumer wine preference profile for the user. Theconsumer wine preference profile may identify and link the user with theuser's associated user wine database. The registration module 210 mayissue a username, password, account number of the like. The registrationmodule 210 may allow for communication of wine rankings and otherinformation via a mobile device or any other device.

If registration is successful (e.g., sufficient user information isprovided), the registration module 210 may trigger the training module204 to request information regarding past wines consumed by the userand/or other experiences. Subsequently, the training module 204 maygenerate and/or train the user database.

The user profile database 212 may include any database(s) or other userstructure(s) to store user databases. As discussed herein, a userdatabase and/or the consumer wine preference profile may include anyinformation regarding a user's past experiences with wine, includingpast wines consumed, user scores of the wine, past wine requests, pastwine recommendations and rankings, location of the user, price pointpreferences of the user, user intensity preference values, and the like.In some embodiments, the user database comprises a wine proxy for theuser based on information as described herein.

Those skilled in the art will appreciate that the user profile database212 may be remotely located from the user and/or the wine ranking system108. In some embodiments, the user profile database 212 may be stored inthe user's digital device.

The wine database 214 may include the wine database as described herein.The wine database 214. The wine database 214 may include any database(s)or other user structure(s) to store one or more wine databases. The winedatabase 214 may be remotely located from the user and/or the wineranking system 108.

A module is any hardware, software, or combination of both hardware andsoftware. Those skilled in the art will appreciate that the modulesidentified in FIG. 2 may perform more or less functionality as describedherein. For example, some modules may perform the functions of othermodules. Further, functions shown with respect to FIG. 2 are not limitedto a single digital device but may be performed by multiple digitaldevices performing different functions. In some embodiments, multipledigital devices perform functions simultaneously.

In some embodiments, one or more of the functions described herein maybe performed on the user's digital device 102. For example, instead ofsending an update request, the user may input the update informationinto the digital device 102 which may, in turn, generate the userdatabase and/or wine proxy. The wine proxy or any other information maybe provided to the wine ranking system 108 to receive a recommendationor wine ranking. In some embodiments, some information is provided tothe digital device 102 which subsequently may apply the wine proxy,select wines, and/or rank wines. Those skilled in the art willappreciate that the functions described herein may be performed bydifferent devices in any number of ways.

FIG. 4 is a block diagram of a digital device 102 in some embodiments.The digital device 102 may comprise a communication module 400, a GUImodule 402, a wine request module 404, a wine categorical classificationmodule 406, a location module 408, an update module 410, a wineselection module 412, and a local database 414.

In some embodiments, the digital device 102 is a mobile device such as asmart phone, tablet, or the like. The digital device 102 may comprise anapplication, app, or any other functionality to communicate with thewine ranking system 108 and provide wine selections and/or rankings. Inone example, the communication module 400 comprises a browser configuredto access a web page provided by the wine ranking system 108. Thebrowser may be used to register, provide training information, provideupdate information, request wines, and/or request wine rankings. Thecommunication module 400 may comprise any hardware or softwareconfigured to communicate with the wine ranking system 108.

In some embodiments, the communication module 400 communicates with thewine ranking system 108 over an encrypted link to protect user privacyand information. For example, a user may digitally sign communication orestablish an encrypted connection with the wine ranking system 108 priorto registration, training the user wine preference profile, updating theuser wine preference profile, and/or requesting wine rankings from thewine ranking system 108. Examples of encrypted technologies that may beutilized include, but are not limited to, hypertext transfer protocolsecure (HTTPS), VPN, SSL, or the like.

The GUI module 402 may provide a graphical user interface to the user.The GUI module 402 may be a part of a wine ranking application orprovide an interface for the user to provide and receive information. Insome embodiments, the GUI module 402 may utilize one or more APIs of thewine ranking system 108 and/or any other device or software.

The wine request module 404 may provide a wine request to the wineranking system 108. In one example, the user may activate a wine rankingapplication on a smartphone. The user may request a wine selectionand/or a ranking of wines with the wine request module 404. The winerequest module 404 may generate a wine request including a useridentifier (i.e., an identifier of the user, the digital device 102,and/or the application providing the request) as well as categoricalclassifications for a desired wine (e.g., red wine). In someembodiments, the wine request module 404 generates a wine requestincluding a type of food or other information that may assist in thedetermination of a selected wine or assist in the ranking of wines.

The wine categorical classification module 406 may provide a pull downmenu or other selection options to assist the user in selecting relevantinformation that may affect wine selection and/or ranking of wines. Asdiscussed herein, a user is not required to be sophisticated, rather,the user may have a general appreciation of wine. The wine categoricalclassification module 406, may provide the user with a vocabulary tohelp the user identify the desired wines. Selections provided by thewine categorical classification module 406 may be incorporated withinthe wine request by the wine request module 404.

The location module 408 may provide location information within the winerequest provided by the wine request module 404. In various embodiments,the user may provide a location, such as restaurant, winery, or winestore information (e.g., identifying the restaurant, winery, or winestore), within the wine request. The wine request module 404 may providethe wine request, including the location, to the wine ranking system108.

In various embodiments, the wine ranking system 108 may select winesbased on the user's wine proxy and the categorical classifiers discussedherein. The wine ranking system 108 may select a subset of selectedwines based on the location information (e.g., wines that are availableat the location identified in the wine request). The subset may beranked as discussed herein.

The update module 410 may be configured to generate and provide anupdate request to the wine ranking system 108. In various embodiments,the update module 410 and/or the GUI module 402 provides an interface toallow the user to input an identifier which identifies a previouslyconsumed wine. The interface may include a field for the user to inputthe name of the wine. In some embodiments, the interface may include alist of possible wines (e.g., via a pull down menu or with radiobuttons) or a grouping of fields and lists (e.g., a field for the nameof the wine and pull down menus for the vintage and varietal). Theupdate module and/or the GUI module 402 may also provide the user with aselection of options to score or otherwise rate the wine (e.g., 1-5stars). Those skilled in the art will appreciate that the update module410 may provide the wine ranking system 108 with any information toupdate the user database.

In various embodiments, the update module 410 may update the userdatabase and/or update the wine proxy locally utilizing methodsdescribed herein.

The wine selection and ranking module 412 is configured to receive awine selection and/or ranking from the wine ranking system 108. Invarious embodiments, the wine ranking system 108 provides wineselections and/or rankings in response to a wine request received fromthe wine request module 404. The wine selection and ranking module 412may retrieve the wine selections and/or rankings received from the wineranking system 108 and provide the results to the user. Those skilled inthe art will appreciate that a single wine selection received from thewine ranking system 108 may be termed a wine recommendation.

In some embodiments, the wine selection and ranking module 412 mayprovide pricing and/or availability information to the user. Forexample, once wines are selected and ranked, the wine selection andranking module 412 and/or the wine ranking system 108 may retrieveavailability and/or pricing information for the ranked wines (or thewines ranked in the top ten). Pricing information may be retrieved fromany source including retail stores, distributors, or collectedinformation by the wine ranking system 108. Further, the wine selectionand ranking module 412 may provide availability information to the userbased on location information from the location module 408, the digitaldevice's 102 location, location information provided in the userregistration (e.g., based on state, city, or zip code), or generalavailability.

In some embodiments, the GUI module 402 may provide a preference ratiobased on price and ranking. For example, the GUI module 402 may displayhighly ranked wines that are available under $20 with different colors,animations, a score (e.g., similar to a PE ratio in a stock), or otherindicator that may encourage the user to try the wine, even if the wineis not identified as the highest ranked wine based on the user'shistorical wine characteristic preferences.

The local database 414 may comprise all or part of the consumer winepreference profile or user wine database. In some embodiments, the localdatabase 414 may store information that may be used by the update module410 (e.g., past wine preferences, past wine tried, purchase history, orthe like). In one example, the update module 410 may provide updateinformation periodically. For example, the update module 410 may provideupdate information after a predetermined duration, at predeterminedtimes, or after a predetermined amount of information is gathered. Inthis example, information may be stored in the local database 414 atleast until the update module 414 provides the update request to thewine ranking system 108.

In some embodiments, one or more of the functions described herein maybe performed on the wine ranking system 108. Those skilled in the artwill appreciate that the functions described herein may be performed bydifferent devices in any number of ways.

FIG. 5 is a flowchart of a method for generating a wine database in someembodiments. By utilizing a panel of experts and/or trained individualsutilizing a common set of descriptors, individual taste preferences andother subjectivity may be reduced. Further, the collective scoring ofthe descriptors by the trained experts and/or individuals based onobservation allows for objective weighting of the descriptors. As aresult, a wine database may be generated and utilized to more accuratelyselect and rank wines based on the user's experiences and tastes.

In step 502, a plurality of wines are described utilizing descriptors.In various embodiments, a common set of descriptors (e.g., a corpus ofdescriptors) may be identified based on different wine characteristics(e.g., acidity, tannins, structure, alcohol, terroir, and the like). Thecommon set of descriptors may be used to describe all wines. Forexample, each wine may be associated with different intensity valuesassociated with the different descriptors. As such, all wines may becommonly scored. The descriptors may be determined in any number ofways. Further, there may be any number of descriptors. In someembodiments, there are twenty-seven different descriptors that may beassociated with intensity values to describe one wine.

Those skilled in the art will appreciate that the common set ofdescriptors may be defined using any methodology. For example,descriptors may be identified and included in the set based on commonexperience of the experts, ease of communication, and/or utility ofmeaning.

In step 504, the wine description module 202 is configured to receivepredetermined intensity values for each descriptor. In some embodiments,experts or other individuals are trained to utilize the common set ofdescriptors as well as a scale for intensity values. Once trained, theexperts and/or other individuals may taste a variety of different winesand individually assign intensity values for each descriptor associatedwith each wine. Once the intensity values for the descriptors associatedwith one or more wines is determined, the wine description module 202may receive the intensity values. For example, the wine descriptionmodule 202 may directly receive the intensity value data and/or tastingnotes (e.g., the wines tasted, descriptors, and/or the assignedintensity values) from the experts and/or individuals. The winedescription module 202 may be configured to translate the tasting notesfrom the experts and/or individuals into intensity values using NLP asdiscussed herein.

In some embodiments, the intensity values are averaged or combined inany number of ways. In various embodiments, the intensity values are notaveraged but are maintained separately until a sufficient amount ofinformation for each wine is gathered and then the information regardingthe respective wine may be averaged or otherwise combined.

In step 506, the wine description module 202 generates a wine databasebased on the common set of descriptors and the predetermined intensityvalues (i.e., the intensity values provided by the experts and/orindividuals described herein). In various embodiments, the winedescription module 202 generates any number of databases or other datastructures that contain any number of vectors associating intensityvalues with wine descriptors and/or wine identifiers.

Those skilled in the art will appreciate that the wine database may becontinuously updated based on new tastings of previously tasted wines byexperts and/or individuals. In this example, one or more experts maytaste a previously tasted wine and provide intensity values that may becombined and/or added with the previous intensity values (e.g., previousintensity values of a particular descriptor may be combined with the newinformation and the average recalculated).

In some embodiments, the wine description module 202 may age wine and/orinformation regarding previous tastings. Those skilled in the art willappreciate that certain wines may improve or otherwise change over time.The wine description module 202 may apply less weight to previoustasting information (e.g., apply less weight to previous intensityvalues associated with a previous wine tasting past a predeterminedduration of time) and apply more weight to current tasting information(e.g., apply equal or increased weight to intensity values associatedwith a more current wine tasting). In some embodiments, the winedescription module 202 may be configured to reduce weights of someintensity values associated with descriptors that tend to reduce overtime and, similarly, may be configured to increase weights of someintensity values associated with descriptors that tend to increase overtime).

FIG. 6 is a flowchart of a method for training a user database in someembodiments. Those skilled in the art will appreciate that, unlikesystems in the prior art, various embodiments described herein rely onobjective descriptions of wines in terms of descriptors.

In step 602, once the user has registered with the wine ranking system108, the wine ranking system 108 may receive one or more customer winecharacteristics and/or preferences. For example, the user may provideinformation regarding past wines that the user has tasted. In someembodiments, the user may provide only general information (e.g.,categorical classifications) including wine type, varietal, or othergeneral information. The user provide other categorical classificationsincluding, for example, specific wine information such as wine maker,winery, specific name, vintage, or any other information.

In step 604, the wine description module 108 may estimate wine charactercovariance. For example, a wine character covariance may be an N×Nmatrix (where N is the number of descriptors) for the wines identifiedin step 602. In some embodiments, if the user provides sufficientinformation, intensity values may be included for sufficientlyidentified wines (e.g., from the wine database). Intensity values may beestimated based on the wine information provided the user (e.g., basedon wines that are information provided by the user such as a similarwine maker, winery, specific name, region of wine, vintage, or otherinformation).

The wine character covariance may be based on the total number of winesidentified by the user, the wine mean character intensity vectorcomputed for all N characters across the identified wines, and characterintensity values for each of the wines. As discussed herein, may be anestimate of the true covariance of all wines (including those that arenot in the database).

In step 606, the wine description module 108 determines principalcomponents. The principal components of the training database isdetermined by solving the Eigenvalue problem for N wine charactersutilizing on a matrix that contains N Eigenvectors (i.e., principalcomponents) of the de-correlated user wine parameter basis.

In step 608, the wine description module 108 approximates variance ofwine characteristics. For example, the wine description module 108 maypick a subset of Eigenvectors to recue ambiguity or uncorrelated noise.The new Eigen-characters may approximate variance of winecharacteristics. The calculated variance may be utilized as the user'sindividual “wine proxy” as the set of Eigen-characters, mean charactervector, and relative importance values (i.e., filter weights).

In step 610, the wine description module 202 stores the variance (e.g.,the user's “wine proxy”) within the user's preference profile. The winedescription module 202 may store the information within the user profiledatabase 212, the wine database 214, and/or on the user's digital device(e.g., digital device 102).

FIG. 7 is a flowchart of a method for a user receiving ranked wines on auser's digital device 102 in some embodiments. In various embodiments,the user may indicate a desire to receive a list of ranked wines. Theuser may engage an application or contact the wine ranking system 108 toprovide a wine request. In step 702, the GUI module 402 displaysavailable descriptors and/or categorical classifications to the user. Inone example, the user may be encouraged to select one or more of theprovided categorical classifications to indicate the type of wines to beranked.

For example, the GUI module 402 may provide a list of descriptions ofwines including possible wine regions of preferred wines, color ofwines, common vintages, or the like. In some embodiments, a limited setof categorical classifications may be provided to the user to train theuser wine database. The wine ranking system 108 may comprise translatorsto associate the user's selected categorical classifications with anynumber of descriptors and/or related intensity values. In someembodiments, the intensity values may be neutral until or unless theuser indicates a degree of preferences (e.g., number of stars or otherindication of preference). In some embodiments, the GUI module 402provides fields that the user may provide text input identifyingdescriptors and/or categorical classifications.

In step 704, the GUI module 402 may receive a selection of one or morecategorical classifications from the user. In step 706, the wine requestmodule 404 may generate a wine request including an identifier (e.g.,identifying the user, a related user account, and/or the device toreceive the wine ranking). The wine request may also include thecategorical classifications from the user. In some embodiments, the winerequest includes the contents of fields input by the user.

In step 708, in response to the wine request, the wine selection andranking module 412 may receive a wine request response in response fromthe wine ranking system 108. The wine request response may comprise arecommended wine (e.g., a selected wine) or a list of ranked wines basedon the provided information as well as the previously providedinformation from the user. The list may comprise wine identifiers rankedby the user's preference as represented by the user wine database.

In step 710, the GUI module 402 displays ranked wine identifiers (e.g.,the ranked list) from the wine request response. In various embodiments,the GUI module 402 may reorder the list or re-rank the list based onavailability, location of the digital device, price, or any preferencesnot provided by the wine request module 404. In some embodiments, theGUI module 402 allows the user to filter the ranking in any number ofways.

In step 712, the update module 410 may receive a user's wine identifierselection (e.g., a selection of at least one of the ranked wines)indicating that the user has chosen to try the selection. In step 714,the update module 410 may provide the selection to the wine rankingsystem 108 to update the user's wine database and/or wine proxy. In someembodiments, the user may provide a score (e.g., star rating) to a wineat the time the update module 410 provides the selection. Alternately,the user may provide the scoring at a later time (e.g., the wine rankingsystem 108 may provide a message requesting that the user provide ascore to the wine identified by the update module 410).

In various embodiments, the user may update the user wine databaseand/or consumer wine preference profile utilizing a card such as aloyalty card and/or credit card during purchases. For example, a usermay provide such a card during wine purchases. An employee at arestaurant, retail establishment, winery, or the like may scan the card.As a result, the wine ranking system 108 may receive an indication ofwines purchased by the user during the transaction. The wine rankingsystem 108 may updated the user's consumer wine preference profile basedon the information. In some embodiments, the wine ranking system 108 mayprovide the user with an email or other communication requesting thatthe user provide a score or other indication of user preference whichmay be used to weight intensity values associated with the purchasedwine(s). In some embodiments, if no response is received, the wineranking system 108 may apply a neutral weight or disregard thepurchases.

Those skilled in the art will appreciate that the purchase may not belimited to cards, but may include providing a code or other identifierduring online purchases. Digital passports or wallets (e.g., possiblyutilizing NFC communications) may similarly be used to provide purchaseinformation to the wine ranking system 108. Moreover, in someembodiments, an application may be provided that allows a user's mobiledevice to scan bar codes and/or provide photographs of labels to allowthe wine ranking system 108 to update the consumer wine preferenceprofile. In this example, the wine ranking system 108 may include a barcode database that allows the wine ranking system 108 to identify therelated wine. Similarly, the wine ranking system 108 may include ascanning module configured to process images from smartphone cameras toidentify wines. Various labels and/or label information may be storedwithin one or more databases. The label information may be utilized toidentify at least portions of a label to allow for matching (e.g.,utilizing hashed information of the labels for matching) and wineidentifications.

FIG. 8 is a flowchart of a method for providing ranked wines to theuser's digital device 102 in response to a wine request in someembodiments. In step 802, the wine ranking system 108 receives a winerequest from the digital device 102. In various embodiments, the rankingmodule 206 receives the request. The request may comprise an identifierthat identifies the user (e.g., username, password, and/or accountnumber) or the digital device 102.

In step 804, the ranking module 206 may retrieve the consumer winepreference profile based on the identifier from the wine request. Theconsumer wine preference profile may comprise the user's precalculatedwine proxy and/or other information. In some embodiments, the rankingmodule 206 retrieves the user's wine database based on the consumer winepreference profile.

In optional step 806, the ranking module 206 may retrieve a subset ofdescriptors and/or predetermined intensity values from the wine databasebased on information contained in the wine request. For example, theuser may include location information and/or categorical classificationinformation within the wine request. The ranking module 206 may retrievea subset of descriptors and/or predetermined intensity values from thewine database that are consistent with the location information (e.g.,available at the user's current location) and/or categoricalclassification (e.g., white wine from France).

In some embodiments, the ranking module 206 may retrieve all descriptorsand predetermined intensity values from the wine database and correlatethe information with the user's wine proxy (e.g., the user preferenceintensity values). After wines are selected based on the correlation,the ranking module 206 may filter the results to remove wine that arenot associated with the location and/or categorical classifications ofthe wine request. In some embodiments, the filtering may occur after thewines are ranked. In some embodiments, the entire selection and/orranking may be provided to the digital device 102 of the user whichperforms the filtering step.

In step 808, the ranking module 206 may retrieve at least a subset ofthe user preference intensity values based on the categoricalclassifications within the wine request. For example, the ranking module206 may select only those user preference intensity values associatedwith wines that meet the categorical classifications. In someembodiments, the ranking module 206 retrieves all of the user preferenceintensity values and/or the user's wine profile for all wines identifiedby the user.

In step 810, the ranking module 206 correlates the user preferenceintensity values with predetermined intensity values from the winedatabase as retrieved in step 806. The ranking module 206 may select anynumber of wines based on the correlation. The correlation process isdescribed herein.

In step 812, the ranking module 206 selects and ranks two or more winesbased on the correlation. As discussed herein, the system may rank winesbased on an objective assessment calibrated to the user's preferencesbased on the user's personal experience. In step 814, the ranking module206 may provide identification(s) of the selected wines (e.g., a rankedlist) to the digital device 102.

FIG. 9 is a flowchart of a method for updating a user wine database insome embodiments. In various embodiments, the wine ranking system 108receives a wine update request from a digital device 102. The wineupdate request may comprise a wine identifier and/or a preference rating(e.g., a score of 1-5 indicating the user's preference or enjoyment ofthe identified wine). Those skilled in the art will appreciate thatthere may be any range of ratings (e.g., 1-4 stars, 1-10 points, or thelike). Further, those skilled in the art will appreciate that rankingsmay be any numerical or non-numerical value.

In step 904, the update module 208 may retrieve the wine identifier andthe preference rating. The update module 208 may incorporate the newobservations into the user's wine proxy. In some embodiments, theprocess is similar to a mathematical regression whereby a new lambda isgenerated (see equation 7 herein) which updates the user's lamba forfuture rankings in step 906. The wine average <ch> is also updated. As aresult, as new wines are tried and added to the system, the rankings,selections, and/or recommendations of the wine ranking system 108 mayimprove.

Those skilled in the art will appreciate that systems and methodsdescribed herein may be applied outside of wine. In various embodiments,systems and methods described herein may be utilized to recommend and/orrank food and wine pairings, foods in general, or the like. In someembodiments, a “pseudo-wine” may be calculated based on shareddescriptors between wines and food. For example, there may be eightdescriptors that related to food and wine. The eight descriptors may beused to describe the characteristics of the food in question. Differentfoods with different intensities (i.e., intensity values) related todescriptors may be used to create a “food proxy” in a manner similar tothe construction of the wine proxy (e.g., utilizing the wine descriptorsand associated intensity scores for the food(s) of interest). Similaritybetween the food proxy and wines in the wine database may be utilized toprovide a wine pairing recommendation. Those skilled in the art willappreciate that any number of descriptors may be utilized to describefood.

In one example, experts and/or trained individuals may establish or mayutilize a common parameter set of descriptors to describe foods. Theexperts and/or trained individuals may taste a wide variety of foodsand/or types of one or more foods (e.g., meats). The experts and/ortrained individuals may score intensity values based on the winedescriptors to describe the food. This information may be used totranslate a request for a food and wine pairing (e.g., a request tomatch rib eye steak and wine) into a food proxy as described herein thatmay be utilized for the similarity assessment, selection, and/orranking.

Various embodiments described herein may be utilized to apply to foods(e.g., olive oils or combinations of different foods such as differentmeats combined with other non-meat consumables) or other goods. Forexample, the ranking module 206 of the wine ranking system 108 describedwith regard to FIG. 2 may be configured to identify foods of interest tousers based on descriptors and user preference intensity values from anassociated user database (e.g., user profile). Similar to the winemethod, the ranking module 206 may take parametric information(characters and intensities) from the user training database, perform aspatial correlation across parameters and food entries, and use theresulting statistical correlations to mathematically reduce theparameter set to a limited number of new uncorrelated variables which,taken in linear combination, may uniquely define the user's foodpreference (i.e., the user food proxy).

Describing an approach utilizing principal component analysis as oneexample, the mathematical procedure transforms, a number of correlatedvariables (i.e., food characters in this case) into an equal number ofuncorrelated variables (vectors) (principal components) whilemaintaining full variance and ordering the components by contribution.The resulting transformation may be such that the first principalcomponent represents the largest amount of variability (i.e., has thelargest weight), while each successive component may account for atleast some of the remaining variability.

For example, a training database, similarly generated as that of thetraining database for the wine example, has M foods and that each foodhas N characters (e.g., descriptors). A food character covariance (N×N)matrix then can be estimated from the training database according to theapproximation:

$\begin{matrix}{C = {{1/M}{\sum\limits_{i = 1}^{M}{\left\lbrack {{ch}_{i} - {\langle{ch}\rangle}} \right\rbrack^{T}\;\left\lbrack {{ch}_{i} - {\langle{ch}\rangle}} \right\rbrack}}}} & (8)\end{matrix}$

where M is the total number of foods in the training database,

ch

is the food mean character intensity vector computed for all Ncharacters (i.e., intensity values) across all Mfoods, and ch_(i) iseach character intensity vector (length=N) for each of the M foods indatabase.

Since C is a symmetric semi-positive definite matrix, the principalcomponents of the training database may be computed by solving theEigenvalue problem for the N food characters:

Cλ=λV  (9)

The matrix V contains the N Eigenvectors (i.e., principal components) ofthe de-correlated user food parameter basis. The vector λ contains the NEigenvalues (principal component weights representing the relativeimportance of each individual Eigen-character, V_(i), in describing theuser's food “type”).

The ranking module 206 may pick a small or smallest subset, P<<N, ofEigenvectors from this base that adequately account for most of our foodcharacter variability according to the criteria, e.g.:

Σλ_(i)[1:P]/trace[C]≧70%.  (10)

Whether the system uses all N or just P components of the de-correlatedbasis, these new food Eigen-characters approximate the variance (and toa lesser extent the correlation) of food characteristics follow themathematical form:

Var[food]_(user) =

ch

+λ _(user) V  (11)

In this context, the larger each λ_(i), the more important (and morecorrelated across the database) each component, V_(i), may be indescribing the likes of the user for the particular set of foods in thetraining.

As similarly described herein, equation 4 may project the foodcharacters into a new mathematical space (i.e., the user “proxy space”)that exploits the statistical relationship between different foodcharacters.

Those skilled in the art will appreciate that the largest Eigenvalue(λ₁) in equation 4 may represent the least distinguishing proxycharacter for food, because all food may share this character (this v₁represents the maximum correlation between all wines in the subset),while the smallest Eigenvalue (λ_(N)) may represent the mostdistinguishing proxy character, because it is correlated between foodsless than all other food characters—it may be the most uniqueEigen-character.

In some embodiments, matrix V is consistent with equation 2 and specificto the user. The statistical proxy may include the user's λ values, V,and CH. The ranking module 206 may utilize this process to create abasis for initial ranking of food.

Once the user proxy is computed, future user food requests may befiltered by the operator V in order to transform all foods from a new“dynamic” database into the user's proxy space. To this end, equation 4may allow the user to specify new food descriptors they are currentlyinterested in having the system rank. The update module 208 then usesthis information to build a dynamic database which is distinct from thetraining discussed herein. In one example, the update module 208 updatesthe existing user database with foods to those of current interest. Thenthe update module 208 “projects” each food (e.g., the update module 208projects each wine's characteristics as defined herein) contained inthis dynamic database to the user proxy space by solving the small (P×P)principal component (PC) problem:

λ_(food) _(i) =[ch _(food) _(i) −

ch

]V  (12)

Here, λ_(food) is each of i foods PC defined by each character vector,ch_(food), contained in the dynamic database and filtered by theEigen-vector operator V.

Then the system can rank (in either ascending or descending order) all ifoods from the dynamic database according to their mathematicalsimilarity/difference, S_(i), in the proxy space to the previouslydefined user food proxy values, λ_(user), from equation 4:

s _(i)=Σ_(i=1) ^(p)|λ_(user)−λ_(food) _(i) |  (13)

In various embodiments, every food in a database that matches a searchmay be assessed. In one example, foods are retrieved that match a searchbased on a user food request and then the related descriptors may beconverted to a mathematical space to look for similarity with thestatistical proxy.

Retrieved (e.g., selected) foods may be ranked based on the similarityto the statistical proxy. The identifiers (e.g., labels, names, or thelike) of the foods may be ranked. In some embodiments, when the rankedfoods are provided, food identifiers, location where the food isavailable, degree of similarity, and/or pricing may be provided to theuser. In some embodiments, the ranking module 206 may provide a valuenumber based on price and fitness.

As discussed herein regarding wine, the user food proxy may then beupdated to reflect these user feedback ratings by solving a regressionproblem (mathematical fitting problem). This technique (which has manyembodiments) may incorporate new observations (user ratings) into theuser proxy vector (λ) via the general mathematical form:

[λ_(update) ]=[λR _(W)λ^(T) +εI] ⁻¹λ^(T) R _(W) C  (14)

As discussed herein, R_(W) is a diagonal weighting matrix containing therelative user ratings for each wine, I is identity matrix, ε is adamping term for stabilization, C is the vector containing the sum ofeach wine vector residual (projected into the proxy space) for all wines(stored by the system from the previous training and ranking steps), andλ is per equation 5 for each wine. This updated λ_(update) is used toupdate λ_(user) (λ_(new)=λ_(update)), is stored by the system, andreplaces λ_(user) in all future Step 4 rankings. The average

ch

is also updated accordingly from the composite list of all wines ratedand in the dynamic database. Then as the user tries/rates more wines,the system will better adapt to the user's likes/dislikes and rankingswill increase in accuracy going forward.

In various embodiments, the food ranking system 108 provides a rankingof foods based on a subset of the foods in the food database. Forexample, the user may request foods that are available based on location(e.g., restaurant, wine bar, or the like) and/or based on categoricalclassifications (e.g., meat, vegetable, texture, or consistency). Insome embodiments, the system may select a subset of the wine database tocorrelate with the user's food proxy.

Those skilled in the art will appreciate that systems and methodsdescribed herein may not be limited to consumable good such as food anddrink, but may be extended to ranking and/or recommendation of couponsor the like.

FIG. 10 depicts an exemplary digital device 1000 according to someembodiments. The digital device 1000 comprises a processor 1002, amemory system 1004, a storage system 1006, a communication networkinterface 1008, an I/O interface 1010, and a display interface 1012communicatively coupled to a bus 1014. The processor 1002 may beconfigured to execute executable instructions (e.g., programs). In someembodiments, the processor 1002 comprises circuitry or any processorcapable of processing the executable instructions.

The memory system 1004 is any memory configured to store data. Someexamples of the memory system 1004 are storage devices, such as RAM orROM. The memory system 1004 may comprise the RAM cache. In variousembodiments, data is stored within the memory system 1004. The datawithin the memory system 1004 may be cleared or ultimately transferredto the storage system 1006.

The storage system 1006 is any storage configured to retrieve and storedata. Some examples of the storage system 1006 are flash drives, harddrives, optical drives, and/or magnetic tape. In some embodiments, thedigital device 1000 includes a memory system 1004 in the form of RAM anda storage system 1006 in the form of flash data. Both the memory system1004 and the storage system 1006 comprise computer readable media whichmay store instructions or programs that are executable by a computerprocessor including the processor 1002.

The communication network interface (com. network interface) 1008 may becoupled to a data network (e.g., communication network 106) via a link.The communication network interface 1008 may support communication overan Ethernet connection, a serial connection, a parallel connection, oran ATA connection, for example. The communication network interface 1008may also support wireless communication (e.g., 802.11a/b/g/n, WiMAX). Itwill be apparent to those skilled in the art that the communicationnetwork interface 1008 may support many wired and wireless standards.

The optional input/output (I/O) interface 1010 is any device thatreceives input from the user and output data. The optional displayinterface 1012 is any device that may be configured to output graphicsand data to a display. In one example, the display interface 1012 is agraphics adapter.

It will be appreciated by those skilled in the art that the hardwareelements of the digital device 1000 are not limited to those depicted inFIG. 10. A digital device 1000 may comprise more or less hardwareelements than those depicted. Further, hardware elements may sharefunctionality and still be within various embodiments described herein.In one example, encoding and/or decoding may be performed by theprocessor 1002 and/or a co-processor located on a GPU.

In various embodiments, a system accurately predicts tastes andsensations of users who experience wines. A panel of people who haveexperience with wines may be provided with a variety of wines in arelevant order. The order may be related to a specific flight and/orpairings. The panelists' tastes and sensations may be recorded usingdata entry techniques, such as techniques involving Scantron® sheets,punch cards, and electronic devices. The tastes and sensations maycorrespond, for example, to descriptors associated with one or moreproperties of wine. Once recorded, a multitude of panelists' responsesmay be evaluated to determine global intensity values of eachdescriptor. Using statistical measures, the global intensity values mayapproximate objective measures of these descriptors.

A panelist's individual score(s) (e.g., a panelist's intensity value)associated with a descriptor may be excluded if the score(s)significantly deviate from global intensity scores, deviate from thepanelist's scores related to similar descriptors, or suggest thepanelist is unable to accurately score the wine descriptors. Further, insome embodiments, a panelist's bias may be detected and accommodated(e.g., corrected and/or updated) by normalizing intensity scoresprovided by the panelist. The updated global intensity scores may beused to populate the user wine database (e.g., the wine database 214 inFIG. 2) as discussed herein.

FIG. 11 depicts an environment 1100 that facilitates identifyingproperties of wines and matching wines with the preferences of users.The environment 1100 includes a first digital device 102, a seconddigital device 104, a communication network 106, a wine ranking system108, and a wine panelist data processing system 1102. The first digitaldevice 102, the second digital device 104, and the wine ranking system108 are coupled to the communications network 106. In variousembodiments, each of the first digital device 102, the second digitaldevice 104, the communications network 106, and the wine ranking system108 corresponds to its respective counterpart in FIG. 1.

The wine panelist data processing system 1102 may be coupled to thecommunications network 106. The wine panelist data processing system1102 may be implemented using a digital device. As discussed, a digitaldevice is any device with memory and a processor. The wine panelist dataprocessing system 1102 may comprise a mobile or stationary digitaldevice such as, but not limited to: a desktop computer, a laptopcomputer, a tablet computing device, a smartphone, a networked server, adedicated server, and/or portions of a distributed server. The winepanelist data processing system 1102 may include any number of digitaldevices (e.g., servers). In various embodiments, the wine panelist dataprocessing system 1102 comprises an application that communicates withthe wine ranking system 108. For example, the wine panelist dataprocessing system 1102 may comprise a standalone computer applicationthat is executed on the wine panelist data processing system 1102,and/or portions of a web page that are displayed in a containerapplication (e.g., an Internet browser or a sandboxed computingenvironment) on the wine panelist data processing system 1102.

In some embodiments, wine panelist data processing system 1102 comprisesa mobile application. In some implementations, the wine panelist dataprocessing system 1102 comprises or is associated with a web server thatprovides wine recommendations and/or rankings to the digital device 102via the Internet.

The wine panelist data processing system 1102 may send data to andreceive data from the wine database 214, shown in FIG. 2 and discussedfurther herein. In some embodiments, the wine panelist data processingsystem 1102 provides to the wine database 214 measures (e.g., intensityscores) of wine descriptors (e.g., acidity, alcohol, aroma intensity,baking spices, berries, Brett, candied fruit, citrus, dried fruits jam,earthy, flavor intensity, floral, green notes, hay straw, lees butter,length of finish, mineral, native grapy, palate weight, pepper,perceived sugar, petrol, sweet aromas, texture, tree fruits, tropicalfruits melon, woody) for various wines.

As discussed herein, a wine identifier identifies a particular wine(e.g., Robert Foley Claret 2010). A wine descriptor is a characteristicof a wine. Each wine descriptor may have an associated intensity score.The intensity score represents a degree of actual and/or perceivedpresence of the wine descriptor. An intensity score may be defined as acertain range. For example, an intensity score may be zero to six, withzero indicating that a wine characteristic related to the winedescriptor is not present and a six being a maximum amount of the winecharacteristic related to the wine descriptor. Those skilled in the artwill appreciate that there may be any range or representation ofintensity values.

In some embodiments, the wine panelist data processing system 1102obtains intensity values associated with wine characteristics from oneor more panels of wine tasting panelists. The panelists in a panel mayhave some level of expertise in recognizing the characteristics ofwines. For example, in some embodiments, the wine panelist dataprocessing system 1102 obtains intensity values of wine characteristicsfrom wine experts who have expertise to recognize the characteristics ofwines. The wine experts may have been formally trained in recognizingthe characteristics of wines, may be able to recognize thecharacteristics of wines based on their experience, and/or may bequalified to recognize the characteristics of wines in some other way.

The wine panelist data processing system 1102 may receive intensityvalues from panelists of similar or different levels of expertise inrecognizing the characteristics of wines. In some embodiments, the winepanelist data processing system 1102 receives intensity values frompanelists who have expertise in recognizing only a specific set ofcharacteristics (e.g., only peppers, hay, and perceived sugar) of wines.Further, the wine panelist data processing system 1102 may receiveintensity values from different panels of wine experts for differentvarieties of wines. The wine of a panel may be ordered in accordancewith the method shown in FIG. 40.

In various embodiments, the wine panelist data processing system 1102receives intensity values from panels that are likely to producesubjective information related to wine characteristics. Panels that arelikely to yield statistically meaningful results may have a large numberof panelists. In various embodiments, the wine panelist data processingsystem 1102 may only use panels having dozens, hundreds, or thousands ofpanelists.

Panels with a sufficiently large number of panelists may produceinformation that follows a Gaussian or near-Gaussian distribution.Panels with a sufficiently large number of panelists may utilize means,deviations, and/or other statistically relevant properties related tointensities of wine characteristics in a meaningful manner. By utilizinga sufficiently large number of panelists, it is less likely that theoverall intensity values (e.g., global intensity values) will be skewedby inaccurate, imprecise, and/or biased assessments of a single panelistor a handful of panelists.

In various embodiments, the wine panelist data processing system 1102receives intensity values from panels of “blind” panelists. For example,panels may include panelists who are unaware of a specific variety,vintage, geographic locale, label, price, or producer (i.e., winery) ofwine that is being tested. Although the panelists in a panel may haveexperience in recognizing flavors, they need not know which variety orthe exact flavors in a variety of wine being tested when the variety isbeing tasted.

Panelists may be required to repeat sampling of one or more wines. Forexample, each panelist may be required to sample Robert Foley Claret2010 twice, three times, etc. Repeat sampling may indicate theconsistency or inconsistency of one or more panelists. Adjusting globalintensity values based on consistency may assist in ensuring that theinformation about wine characteristics from the panel is sufficientlyprecise and/or unbiased. In various examples, panels may be constitutedaccording to Tables 2, 3, and 4 shown as follows:

TABLE 2 Example random sequence used for blind scoring. Number ScantronRecord Sequence Letter Number Code 1 1 A 520 A520 2 1 B 520 B520 3 1 C520 C520 4 1 D 520 D520 5 1 E 520 E520 6 1 F 520 F520 7 1 G 520 G520 8 1H 520 H520 9 1 I 520 I520 10 1 J 520 J520 11 1 K 520 K520 12 1 L 520L520 13 1 M 520 M520 14 1 N 520 N520 15 1 O 520 O520 16 2 A 820 A820 172 B 820 B820 18 2 C 820 C820 19 2 D 820 D820 20 2 E 820 E820 21 2 F 820F820 22 2 G 820 G820 23 2 H 820 H820 24 2 I 820 I820 25 2 J 820 J820 262 K 820 K820 27 2 L 820 L820 28 2 M 820 M820 29 2 N 820 N820 30 2 O 820O820 31 3 A 558 A558 32 3 B 558 B558 . . . . . . . . . . . . . . . 13335889  O 128 O128

TABLE 3 An example of Random-number-assignment worksheet. Day 1 Day 2Day 3 Flight Wine Random Flight Wine Random Flight Wine Random VarietalNo. No. No. Varietal No. No. No. Varietal No. No. No. Dry Riesling 1 1160 Chardonnay 7 1 209 Pinot Noir 13 1 310 Dry Riesling 1 2 650Chardonnay 7 2 899 Pinot Noir 13 2 541 Dry Riesling 1 3 667 Chardonnay 73 958 Pinot Noir 13 3 975 Dry Riesling 1 4 225 Chardonnay 7 4 587 PinotNoir 13 4 977 Dry Riesling 1 5 791 Chardonnay 7 5 809 Pinot Noir 13 5755 Dry Riesling 1 6 466 Chardonnay 7 6 796 Pinot Noir 13 6 970 DryRiesling 1 7 969 Chardonnay 7 7 241 Pinot Noir 13 7 562 Dry Riesling 1 8406 Chardonnay 7 8 304 Pinot Noir 13 8 954 Dry Riesling 1 9 963Chardonnay 7 9 351 Pinot Noir 13 9 430 Dry Riesling 1 10 511 Chardonnay7 10 636 Pinot Noir 13 10 625 Pinot Noir 2 1 984 Cab. Sauvignon 8 1 951Merlot 14 1 547 Pinot Noir 2 2 994 Cab. Sauvignon 8 2 824 Merlot 14 2992 Pinot Noir 2 3 965 Cab. Sauvignon 8 3 553 Merlot 14 3 713 Pinot Noir2 4 217 Cab. Sauvignon 8 4 283 Merlot 14 4 897 Pinot Noir 2 5 850 Cab.Sauvignon 8 5 690 Merlot 14 5 998 Pinot Noir 2 6 730 Cab. Sauvignon 8 6367 Merlot 14 6 588 Pinot Noir 2 7 853 Cab. Sauvignon 8 7 604 Merlot 147 971 Pinot Noir 2 8 538 Cab. Sauvignon 8 8 781 Merlot 14 8 269 PinotNoir 2 9 996 Cab. Sauvignon 8 9 306 Merlot 14 9 286 Pinot Noir 2 10 747Cab. Sauvignon 8 10 318 Merlot 14 10 391 Flight no. 3 Flight no. 9Flight no. 15 . . . . . . . . . Flight no. 6 Flight no. 12 Flight no. 18

TABLE 4 An example of sample order with the 3-digit random number for aflight of 10 wines. Wine Random Panelists Randomized wine order for aflight of 10 wines serving 12 panelists No. No. A 253 181 880 479 498238 266 363 484 490 1 490 B 181 479 253 238 880 363 498 490 266 484 2484 C 479 238 181 363 253 490 880 484 498 266 3 266 D 238 363 479 490181 484 253 266 880 498 4 498 E 363 490 238 484 479 266 181 498 253 8805 880 F 490 484 363 266 238 498 479 880 181 253 6 253 G 484 266 490 498363 880 238 253 479 181 7 181 H 266 498 484 880 490 253 363 181 238 4798 479 I 498 880 266 253 484 181 490 479 363 238 9 238 J 880 253 498 181266 479 484 238 490 363 10 363 K 880 498 253 266 181 484 479 490 238 363L 253 880 181 498 479 266 238 484 363 490

In some embodiments, the wine panelist data processing system 1102statistically processes information related to wine characteristics. Thewine panelist data processing system 1102 may identify common patternsin the information produced by panelists. For example, the wine panelistdata processing system 1102 may identify averages, means, medians,deviations, and other statistically relevant patterns related to how oneor more panelists have assessed wine characteristics.

The wine panelist data processing system 1102 is capable to removeassessments and/or intensity values associated with panelists that aredeemed inaccurate or imprecise. The wine panelist data processing system1102 is capable to adjust assessments and/or intensity values deemedbiased. An assessment of a wine characteristic by a panelist's may bedeemed inaccurate if it significantly deviates from how a panel ormultiple panels assess the wine characteristic (e.g., relative to aglobal intensity value). In one example, the wine panelist dataprocessing system 1102 may use statistical measures to deem anassessment by a panelist inaccurate, e.g., if the panelist's intensityvalue of fruitiness of Robert Foley Claret 2010 is two standarddeviations from a global intensity value (e.g., the mean intensity valueof fruitiness of Robert Foley Claret 2010 of the panel). In one example,the wine panelist data processing system 1102 may use statisticalmeasures to deem an assessment of a wine characteristic as imprecise,e.g., if a panelist's first assessment of a wine characteristic of awine significantly deviates from how the panelist previously assessedthe same wine characteristic for the same or similar wine. An example ofan imprecise assessment may include a panelist's first intensity valueof fruitiness of Robert Foley Claret 2010 being five points (e.g., of ascale of six) and the panelist's prior intensity value of fruitiness ofthe same wine (e.g., the Robert Foley Claret 2010) as being two points.

The wine panelist data processing system 1102 may deem an a panelist'sassessment of a particular wine characteristic as biased if thepanelist's evaluation of this characteristic across a significantpercentage (e.g., more than 50%) of wines deviates in a statisticallysignificant way (e.g., one standard deviation, 1.5 standard deviations,fails a statistical hypothesis test such as ANOVA, etc.) from how thepanel at large assesses the wine characteristic across the wines. Anexample of a biased assessment may include a panelist's repeatedintensity value of fruitiness of all tasted wines being one or twopoints higher than the mean intensity value of fruitiness of those winesby a panel. In various embodiments, the wine panelist data processingsystem 1102 may normalize data to accommodate a panelist's bias. In theprevious example, the wine panelist data processing system 1102 maylower the biased panelist's intensity values for fruitiness (e.g., byone or two points) for wines when bias was detected or, alternately, forall wines, a subset of wines (e.g., red wines). Such normalization maybe performed to compute averages, means, medians, deviations, and otherstatistically relevant attributes of assessments.

The wine panelist data processing system 1102 may use the statisticallyprocessed information from the panels to provide specific intensitiesfor specific descriptors for wines in the wine database 214. Morespecifically, the wine panelist data processing system 1102 may provideintensities and/or descriptors as database entries for storage in thewine database 214. As discussed herein, the wine database 214 may beused to match the intensities and/or descriptors with information usersare seeking when they are looking for wines to experience. It will beappreciate that the wine database 214 may be stored on any digitaldevice, such as the first digital device 102, the second digital device,104, the wine ranking system 108, or the wine panelist data processingsystem 1102. Further, the wine database 214 may be stored on any otherdevice coupled to the communication network 106.

Although only two digital devices are depicted in FIG. 11, those skilledin the art will appreciate that there may be any number of users withuser databases and/or associated digital devices. Further, there may beany number of networks 106, wine ranking systems 108, and/or wineranking processing systems 1102.

FIG. 12 depicts a block diagram of a wine panelist data processingsystem 1102, according to some embodiments. The wine panelist dataprocessing system 1102 includes a wine panelist interface module 1202, awine panelist statistical processing module 1204, an accuracy managementmodule 1206, a precision management module 1208, a bias managementmodule 1210, a wine database interface module 1212, and a wine panelprofile database 1214. One or more of the wine panelist interface module1202, the wine panelist statistical processing module 1204, the accuracymanagement module 1206, the precision management module 1208, the biasmanagement module 1210, the wine database interface module 1212, and thewine panel profile database 1214 may be coupled to each other or tocomponents external to the components depicted in FIG. 12. One or moreof the wine panelist interface module 1202, the wine paneliststatistical processing module 1204, the accuracy management module 1206,the precision management module 1208, the bias management module 1210,the wine database interface module 1212, and the wine panel profiledatabase 1214 may include hardware, software, and/or firmware.

The wine panelist interface module 1202 may receive evaluations of winesfrom wine panels. The wine panelist interface module 1202 may include atleast portions of an application that receives evaluations containingwine identifiers, wine descriptors, and intensity values related to winedescriptors for specific wine identifiers. For example, the winepanelist interface module 1202 may include an application to receivenames of wines a panel will taste, and intensity values for winedescriptors of the wines the panelists taste.

The wine panelist interface module 1202 may receive evaluations (e.g.,sets of intensity values) with information related to panels and/orspecific panelists. The information may include identities, backgrounds,experience levels, past tests, and other information related to specificpanelists. The wine panelist interface module 1202 may store informationrelated to tests, panelists, and/or panels in the wine panel profiledatabase 1214. In some embodiments, the wine panelist interface module1202 interfaces with a scanner that receives portions of Scantron®sheets or other sheets related to a panel's tasting. An example of asheet that may be received is shown in FIG. 39. The wine panelistinterface module 1202 may receive information on punch cards or otherconvenient format. In some embodiments, the wine panelist interfacemodule 1202 includes a Graphical User Interface (GUI) that allowspanelists or data entry personnel to electronically enter (e.g., viatextual input or through selection of elements in a menu) wineidentifiers, wine descriptors, and/or intensity values. As an example,the wine panelist interface module 1202 may provide panelists with a GUIgenerated by a mobile application to enter the results of wine tasting.As another example, the wine panelist interface module 1202 may allowdata entry personnel to manually enter the results of a panel's winetasting using a keyboard or other input device.

The wine panelist statistical processing module 1204 may performstatistical analysis on intensity values provided by panelists. The winepanelist statistical processing module 1204 may include libraries thatcompute averages of intensity values. In some embodiments, the winepanelist statistical processing module 1204 may include libraries thatcompute medians of intensity values. The wine panelist statisticalprocessing module 1204 may include libraries that perform statisticalsignificance tests to determine extent specific intensity values deviatefrom other intensity values for the same or similar wine descriptors.

In some embodiments, the wine panelist statistical processing module1204 performs Gaussian analysis on intensity values from panelists. Forexample, the wine panelist statistical processing module 1204 mayidentify means, standard deviations, principal components, etc. ofintensity values from panelists. In some embodiments, the wine paneliststatistical processing module 1204 exposes libraries to other modules,such as one or more of the accuracy management module 1206, theprecision management module 1208, and the bias management module 1210.

The accuracy management module 1206 may evaluate accuracy of theintensity values from a specific panelist. For example, the accuracymanagement module 1206 may gather (e.g., from the wine panel profiledatabase 1214) intensity values a specific panelist has assigned to aspecific descriptor of a specific wine. The accuracy management module1206 may further obtain a global intensity value that represents how alarger group of panelists have measured the specific descriptor of thespecific wine. The global intensity value may comprise an average valueof intensity values from other panelists. The accuracy management module1206 may compare the specific panelist's intensity value to the globalintensity value to determine variation (if any). For example, theaccuracy management module 1206 may determine a number of standarddeviations between the specific panelist's intensity value and theglobal intensity value. In some embodiments, when the panelist'sintensity value deviates from the global intensity value (e.g., thedeviation is greater than a predetermined deviation threshold), theaccuracy management module 1206 may remove the specific panelist'sintensity value, and/or other information related to the specificpanelist (other intensity values by the same panelist, etc.) from thewine panel profile database 1214. In some embodiments, removal of anassessment occurs only if the number of standard deviations exceeds aspecific threshold (e.g., 1 standard deviations, 1.5 standarddeviations, 2 standard deviations, or the like). In some embodiments,removal of a panelist's assessment of a particular descriptor across allwines occurs if a statistically significant number (e.g., 10%, 25%, 50%,etc.) of inaccuracy deviations are identified across the wines.

In various embodiments, the accuracy management module 1206 instructsthe wine panelist statistical processing module 1204 to update theglobal intensity value for the specific descriptor after all winepanelists' assessments deemed inaccurate have been removed. The accuracymanagement module 1206 may store the updated global intensity value inthe wine panel profile database 1214.

The precision management module 1208 may evaluate the precision ofintensity values from a specific panelist. In some embodiments, theprecision management module 1208 gathers from the wine panel profiledatabase 1214 a plurality of intensity values that a specific panelisthas assigned to a specific descriptor of a specific wine in a pluralityof tastings. The precision management module 1208 compares the pluralityof values to one another. The precision management module 1208 mayremove one or more of the plurality of intensity values from the winepanel profile database 1214 if the intensity values sufficiently deviatefrom one another (e.g., two or more intensity values deviate from eachother beyond a deviation threshold). In some embodiments, removal of thepanelist's intensity value occurs only if the standard deviationsexceeds a specific threshold (e.g., 1 standard deviations, 1.5 standarddeviations, 2 standard deviations, or the like). In some embodiments,removal of a panelist's assessment of a particular descriptor across allwines occurs if a statistically significant number (e.g., 10%, 25%, 50%,etc.) of imprecision deviations are identified across the wines.

In various embodiments, the precision management module 1208 instructsthe wine panelist statistical processing module 1204 to update theglobal intensity value for the specific descriptor after all winepanelists' assessments deemed imprecise have been removed. The precisionmanagement module 1208 may store the updated global intensity value inthe wine panel profile database 1214.

The bias management module 1210 may identify bias in the intensityvalues of a specific panelist. In some embodiments, the bias managementmodule 1210 gathers from the wine panel profile database 1214 all of aspecific panelist's intensity values related to a specific winedescriptor or to similar wine descriptors across a plurality of wines.The bias management module 1210 may compare the specific panelist'sintensity values with global intensity values for the same or similarwine descriptors across the plurality of wines. In an embodiment, thebias management module 1210 determines whether a substantial number ofintensity values deviate from global intensity values and form a commondeviation pattern. For example, the bias management module 1210 maydetermine whether a majority of a specific panelist's intensity valuesare consistently one point higher than the global intensity value forthe same or similar wine descriptors across the wines.

The bias management module 1210 may take corrective actions if biasedpanelists are identified. In an embodiment, the bias management module1210 corrects the intensity values of a biased individual based on thecommon deviation pattern. To continue the foregoing example of thespecific panelist whose intensity values were consistently one pointhigher than the global intensity value, the bias management module 1210may reduce the specific panelist's intensity values by one for thoserelevant descriptors (e.g., for those descriptors related to intensityvalues that deviated from global intensity values in a common deviationpattern).

The wine database interface module 1212 may couple the modules of thewine panelist data processing system 1102 to the communications network1106. In an embodiment, the wine database interface module 1212 providesinformation related to wine tests, wine panelists, and/or wine panels tothe wine database 214 (shown in FIG. 2). The wine database interfacemodule 1212 may instruct the wine database 214 to store the relevantinformation in the form of data records that can be accessed by theother modules of the first digital device 102, the second digital device104, and/or the wine ranking system 108.

The wine panel profile database 1214 may include hardware, software,and/or firmware that stores information related to wine tests, winepanelists, and/or wine panels. In various embodiments, the wine panelprofile database 1214 receives information related to wine tests, winepanelists, and/or wine panels from the wine panelist interface module1202. The wine panel profile database 1214 may also provide informationrelated to wine tests, wine panelists, and/or wine panels to the othermodules of the wine panelist data processing system 1102.

It will be appreciated that a module may include hardware, software,and/or firmware.

FIG. 13 depicts a flowchart of a method 1300 for processing winerankings using the accuracy of an evaluation of wines from a winepanelist, according to some embodiments. The method 1300 is discussed inconjunction with the wine panelist data processing system 1102, shown inFIG. 12. It is noted at least some embodiments may include more or fewersteps than the steps shown in the method 1300.

At step 1302, the wine panelist interface module 1202 receives anevaluation of wines from a wine panelist. In an embodiment, the winepanelist interface module 1202 receives evaluations identified on aScantron® sheet (see, e.g., the sheet in FIG. 39) or punch card. In someembodiments, the wine panelist interface module 1202 receivesevaluations entered in an electronic format. The evaluations may containinformation related to wine identifiers, wine descriptors, and/orintensities related to wine descriptors. In some embodiments, the winepanelist interface module 1202 stores evaluations in a known orconvenient format in the wine panel profile database 1214.

At step 1304, the wine processing statistical panelist module 1204identifies a wine descriptor in the evaluation for the selected wine.More specifically, the wine panelist statistical processing module 1204may identify one or more of the wine descriptors in the evaluations forfurther statistical processing. In an embodiment, the wine paneliststatistical processing module 1204 identifies a single wine descriptor.In other embodiments, the wine panelist statistical processing module1204 identifies groups of wine descriptors that are similar to oneanother and that can be analyzed together. For example, the winepanelist statistical processing module 1204 may identify all winedescriptors that are associated with fruity characteristics.

At step 1306, the wine panelist statistical processing module 1204identifies an intensity value associated with the wine descriptor forthe selected wine. For example, the wine panelist statistical processingmodule 1204 may identity the intensity value a panelist assigned to theidentified wine descriptor. The intensity value may comprise theintensity value provided on the panelist's evaluation. The wine paneliststatistical processing module 1204 may adjust the intensity value sothat it can be compared with a global intensity value for the winedescriptor.

At step 1308, the accuracy management module 1206 determines an accuracydeviation (if any) of the intensity value relative to a global intensityvalue for the wine descriptor for the selected wine. In someembodiments, the accuracy management module 1206 determines a number ofstandard deviations between the intensity value and the global intensityvalue. The accuracy management module 1206 may also determine theaccuracy deviation in other ways, such as computing a simple differencebetween the intensity value and the global intensity value. The accuracymanagement module 1206 may provide the accuracy deviation to the winepanelist statistical processing module 1204.

At step 1310, the accuracy management module 1206 removes the intensityvalue if the accuracy deviation is significant. As discussed, in someembodiments, the accuracy management module 1206 determines the numberof standard deviations between the intensity value and the globalintensity value. If the number of standard deviations meets a specificthreshold (e.g., 1 standard deviation, 1.5 standard deviations, 2standard deviations, or greater) the accuracy management module 1206 maydelete, hide, the intensity value from the wine panel profile database1214 or mark the intensity value in a manner so that the intensity valueis not used for future computations of the general intensity value.

At step 1312, the accuracy management module 1206 removes all intensityvalues for the wine descriptor for the wine panelist if the accuracydeviation of the intensity values of the wine descriptor for the winepanelist is significant across a substantial number of wines. Forexample, if the number of standard deviations meets another specificthreshold (which may or may not correspond to the threshold for removinga single intensity value), the accuracy management module 1206 mayremove all intensity values for the wine descriptor from the winepanelist. As an example, if the panelist's intensity value for hay strawis more than two standard deviations from the global intensity value forhay straw for a given wine, the accuracy management module 1206 mayremove all of the panelist's intensity values for hay straw across allwines.

At step 1314, the accuracy management module 1206 removes the winepanelist if accuracy deviation of the intensity values of the winedescriptor across a substantial number of wine descriptors issignificant. The number of wines required to constitute a “substantial”number of wine descriptors may vary depending on the specificimplementation. However, if a panelist's accuracy deviation on a numberof intensity values of the panelist are inaccurate, the accuracymanagement module 1314 may remove the wine panelist's input from thewine panel profile database 1214.

At step 1316, the wine panelist statistical processing module 1204determines an updated global intensity value without the removedintensity value(s). More specifically, the wine panelist statisticalprocessing module 1204 may recalculate the global intensity valuewithout the contribution of the intensity value(s) removed from the winepanel profile database 1214. Such recalculation may involve calculatingthe mean, median, etc. value of intensity values for wine descriptorsthat were processed by the accuracy management module 1206.

FIG. 14 depicts a flowchart of a method 1400 for processing winerankings using precision of an evaluation of wines from a wine panelist,according to some embodiments. The method 1400 is discussed inconjunction with the wine panelist data processing system 1102, shown inFIG. 12. It is noted at least some embodiments may include more or fewersteps than the steps shown in the method 1400.

At step 1402, the wine panelist interface module 1202 receives first andsecond wine evaluations from a wine panelist for the same or a similarwine. In an embodiment, the wine panelist interface module 1202 receivesfirst and second evaluations from a Scantron® sheet or punch card. Insome embodiments, the wine panelist interface module 1202 receivesevaluations entered in an electronic format. The first and secondevaluations may contain information related to wine identifiers, winedescriptors, and/or intensities related to wine descriptors. In someembodiments, the wine panelist interface module 1202 stores the firstand second evaluations in a known or convenient format in the wine panelprofile database 1214. The first and second wine evaluations may berelated to the same wine or wines with similar characteristics.

At step 1404, the wine processing statistical panelist module 1204identifies a wine descriptor in the first and second evaluations. Morespecifically, the wine panelist statistical processing module 1204 mayidentify a wine descriptor in the first and second evaluations forfurther statistical processing. As an example, the wine paneliststatistical processing module 1204 may identify a wine descriptorcorresponding to floral characteristics.

At step 1406, the wine panelist statistical processing module 1204identifies first and second intensity values from the same panelist, thefirst and second intensity values corresponding to the wine descriptor.For example, the wine panelist statistical processing module 1204 mayidentify a first intensity value for the wine descriptor in the firstevaluation, and a second intensity value for the wine descriptor in thesecond evaluation. The first and second intensity values may correspondto different times the same wine panelist has evaluated the samecharacteristic of the same or similar wine.

At step 1408, the precision management module 1208 determines aprecision deviation (if any) of the first intensity value from thesecond intensity value. More specifically, the precision managementmodule 1208 may determine the extent the first and second intensityvalues differ from one another. In some embodiments, the precisionmanagement module 1208 determines the extent the first and secondintensity values deviate from a global intensity value for the winedescriptor.

At step 1410, the precision management module 1208 removes the first andsecond intensity values from the wine panel profile database 1214 and/orthe global intensity value if the precision deviation is significant. Insome implementations, the precision management module 1208 may delete,hide, the intensity value from the wine panel profile database 1214 ormark the first and/or second intensity values in a manner so that thefirst and/or second intensity values are not used for futurecomputations of the general intensity value. At step 1412, the precisionmanagement module 1208 removes all intensity values for the winedescriptor for the wine panelist if the precision deviation of theintensity values of the wine descriptor of the wine panelist issignificant (e.g., exceeds a predetermined precision deviationthreshold) across a substantial number of wines. At step 1414, theprecision management module 1208 removes the wine panelist if theprecision deviation of the intensity values of the wine panelist isacross a substantial number of wine descriptors is significant.

At step 1416, the wine panelist statistical processing module 1204determines an updated global intensity value without the removedintensity value(s). More specifically, the wine panelist statisticalprocessing module 1204 may recalculate the global intensity valuewithout the contribution of the intensity value(s) removed from the winepanel profile database 1214. Such recalculation may involve calculatingthe mean, median, etc. value of intensity values for wine descriptorsthat were processed by the accuracy management module 1206.

FIG. 15 depicts a flowchart of a method 1500 for processing winerankings using an identified bias on the part of a wine panelist,according to some embodiments. The method 1500 is discussed inconjunction with the wine panelist data processing system 1102, shown inFIG. 12. It is noted at least some embodiments may include more or fewersteps than the steps shown in the method 1500.

At step 1502, the wine panelist interface module 1202 receives anevaluation of wines from a wine panelist. As discussed herein, theevaluation may contain information related to wine identifiers, winedescriptors, and/or intensities related to wine descriptors. In someembodiments, the wine panelist interface module 1202 stores theevaluation in a known or convenient format in the wine panel profiledatabase 1214.

At step 1504, the wine processing statistical panelist module 1204identifies a wine descriptor in the evaluation across a plurality ofwines. More specifically, the wine panelist statistical processingmodule 1204 may identify a wine descriptor in the evaluation for furtherstatistical processing. For example, the wine panelist statisticalprocessing module 1204 may identify the wine descriptor associated withpetrol characteristics.

At step 1506, the bias management module 1210 identifies a plurality ofintensity values associated with the wine descriptor across theplurality of wines. The bias management module 1210 may determine all ora significant number (e.g., beyond a threshold) of the intensity valuesthe wine panelist has provided related to the wine descriptor. Tocontinue the foregoing example, the bias management module 1210 mayidentify all intensity values related to petrol the wine panelist hasprovided for all wines.

At step 1508, the bias management module 1210 determines the winepanelist's accuracy deviation across the plurality of intensity valuesrelative to a global intensity value for the wine descriptor across theplurality of wines. In an embodiment, the bias management module 1210determines the extent the wine panelist's intensity values for the winedescriptor have deviated (if any) from the global intensity value forthat wine descriptor across all wines the wine panelist has tasted. Tocontinue the foregoing example, the bias management module 1210 maydetermine the extent a wine panelist's intensity values for petroldeviate from the global intensity value. An example of such a biasedpanelist may include one who consistently provides an intensity valuefor petrol an average of two points higher than the global intensityvalue for petrol across all wines.

At step 1510, the bias management module 1210 adjusts each of theplurality of intensity values if the panelist's accuracy deviation meetsa predetermined condition. That is, the bias management module 1210 mayraise or lower the panelist's intensity values for the wine descriptorby the number of points the intensity values deviated from the globalintensity value for the wine descriptor across all wines. To continuethe foregoing example, the bias management module 1210 may lower thepetrol-biased panelist's intensity values for petrol by two pointsacross all wines.

At step 1512, the wine panelist statistical processing module 1204determines an updated global intensity value based on the adjustedintensity values. More specifically, the wine panelist statisticalprocessing module 1204 may recalculate the global intensity using theadjusted intensity value. Such recalculation may involve calculating themean, median, etc. value of intensity values for wine descriptors thatwere processed by the accuracy management module 1206.

FIG. 16 depicts a table that illustrates the difference betweenintensity values of two wine panels, according to some embodiments. Thefirst column of the table includes wine descriptors. The second columnof the table shows intensity values of a first wine panel. The thirdcolumn of the table shows the intensity values of a second wine panel.The fourth column of the table shows a measure of the statisticalsignificance (e.g., the p-value) for each wine descriptor. In theexample of FIG. 16, panels were constituted for a variety of red wines.FIG. 17 depicts a table that illustrates the difference betweenintensity values of two wine panels, according to some embodiments. Thetable is similar to the table in FIG. 16, but the panels identified inFIG. 17 were constituted for a variety of white wines. FIG. 18 depicts atable that illustrates the difference between intensity values of twowine panels (e.g., different than those panels of FIG. 16), according tosome embodiments. FIG. 19 depicts a table that illustrates thedifference between intensity values of two wine panels (e.g., differentthan those panels of FIG. 16), according to some embodiments.

FIG. 20 depicts a table that illustrates the difference between thescores by the number of wines per flight, according to some embodiments.The wines for the table are varieties of red wines. FIG. 21 depicts atable that illustrates the difference between the scores by the numberof wines per flight, according to some embodiments. The wines for thetable include different varieties of white wines.

FIG. 22 depicts a table that illustrates average panelist bias for a setof panels, according to some embodiments. FIG. 23 depicts a table thatillustrates the results of wine ranking processing for several varietiesof wine, according to some embodiments.

FIG. 24 depicts a table that illustrates principal component analysis ofa dataset of descriptors, according to some embodiments. It will beappreciated that any statistical model or analytical approach may beused. FIG. 25 depicts a table that illustrates principal componentanalysis of a dataset that excludes non-repeatable descriptors,according to some embodiments. FIG. 26 depicts a table that illustratesprincipal component analysis of a dataset that excludes non-repeatabledescriptors and outliers, according to some embodiments. FIG. 27 depictsa table that illustrates principal component analysis of a dataset thatexcludes outliers, according to some embodiments. FIG. 28 depicts atable that illustrates repeatability analysis of a dataset of wines,according to some embodiments.

FIG. 29A depicts a diagram that illustrates a cluster dendogram and amultidimensional scaling of panelist groupings, according to someembodiments. In the example of FIG. 29A, the dataset for the clusterdendogram and multidimensional scaling is the full dataset. FIG. 29Bdepicts a diagram that illustrates a cluster dendogram and amultidimensional scaling of panelist groupings, according to someembodiments. In the example of FIG. 29b , the dataset for the clusterdendogram and multidimensional scaling includes non-repeatable winedescriptors. FIG. 29C depicts a diagram that illustrates a clusterdendogram and a multidimensional scaling of panelist groupings,according to some embodiments. In the example of FIG. 29B, the datasetfor the cluster dendogram and multidimensional scaling excludesnon-repeatable wine descriptors. FIG. 29D depicts a diagram thatillustrates a cluster dendogram and a multidimensional scaling ofpanelist groupings, according to some embodiments. In the example ofFIG. 29D, the dataset for the cluster dendogram and multidimensionalscaling excludes outliers. These cluster dendograms and multidimensionalscalings become progressively more accurate, precise, and less biased asstatistical analysis on the dataset is performed. For example, thecluster dendogram and multidimensional scaling is more accurate, moreprecise, and less biased than the cluster dendogram and multidimensionalscaling of FIG. 29A.

FIG. 30A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments. FIG. 30B depicts theresults of wine ranking processing for a set of descriptor of wine,according to some embodiments. FIG. 30C depicts the results of wineranking processing for a set of descriptor of wine, according to someembodiments. The results shown in FIGS. 30A-30C include a full datasetfrom a tasting by a panel.

FIG. 31A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments. FIG. 31B depicts theresults of wine ranking processing for a set of descriptor of wine,according to some embodiments. FIG. 31C depicts the results of wineranking processing for a set of descriptor of wine, according to someembodiments. The results shown in FIGS. 31A-31C exclude outliers.

FIG. 32A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments. FIG. 32B depicts theresults of wine ranking processing for a set of descriptor of wine,according to some embodiments. FIG. 32C depicts the results of wineranking processing for a set of descriptor of wine, according to someembodiments. The results shown in FIGS. 32A-32C exclude impreciseintensity values and outliers.

FIG. 33A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments. FIG. 33B depicts theresults of wine ranking processing for a set of descriptor of wine,according to some embodiments. FIG. 33C depicts the results of wineranking processing for a set of descriptor of wine, according to someembodiments. The results shown in FIGS. 33A-33C exclude non-repeatableintensity values and outliers, and have grouped the intensity valuesthat cluster together.

FIG. 34A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments. FIG. 34B depicts theresults of wine ranking processing for a set of descriptor of wine,according to some embodiments. FIG. 34C depicts the results of wineranking processing for a set of descriptor of wine, according to someembodiments. The results shown in FIGS. 34A-34C exclude non-repeatableintensity values, and have grouped the intensity values that clustertogether.

FIG. 35A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments. FIG. 35B depicts theresults of wine ranking processing for a set of descriptor of wine,according to some embodiments. FIG. 35C depicts the results of wineranking processing for a set of descriptor of wine, according to someembodiments. The results shown in FIGS. 35A-35C exclude outliers.

FIG. 36A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments. FIG. 36B depicts theresults of wine ranking processing for a set of descriptor of wine,according to some embodiments. FIG. 36C depicts the results of wineranking processing for a set of descriptor of wine, according to someembodiments. The results shown in FIGS. 36A-36C exclude outliers, andhave grouped the intensity values that cluster together.

FIG. 37A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments. FIG. 37B depicts theresults of wine ranking processing for a set of descriptor of wine,according to some embodiments. FIG. 37C depicts the results of wineranking processing for a set of descriptor of wine, according to someembodiments. The results shown in FIGS. 37A-37C include the fulldataset, and have grouped the intensity values that cluster together.

FIG. 38A depicts the results of wine ranking processing for a set ofdescriptor of wine, according to some embodiments. FIG. 38B depicts theresults of wine ranking processing for a set of descriptor of wine,according to some embodiments. FIG. 38C depicts the results of wineranking processing for a set of descriptor of wine, according to someembodiments. In FIGS. 38A-38C, the red lines show the results ofnon-repeatable intensity values excluded; the yellow lines show theresults of non-repeatable and outlier intensity values excluded; thelime green lines show the results of non-repeatable and outlierintensity values excluded with clusters grouped together; the forestgreen lines show the results of non-repeatable intensity values excludedwith clusters grouped together; the light blue lines show the results ofoutliers excluded; the ocean green lines show the results of outlierintensity values excluded with clusters grouped together; the lavenderlines show results of the full dataset; and the pink lines show theresults with clusters grouped together.

FIG. 39 depicts an example of a wine score sheet for sensory evaluation,according to some embodiments. The wine score sheet comprises aScantron® sheet that a panelist is to fill out. The wine score sheet hastwenty-seven characteristics of wine that the panelist is asked toprovide an intensity value between 0 to 6 for. The wine score sheet alsohas a write-in area for subjective evaluations and/or feedback. The winescore sheet also has a judge identification area to identify thepanelist.

FIG. 40 depicts an example of a workflow process for preparing winepanelists for tasting wines, according to some embodiments. The workflow process may assist wine panelists in conduct a sensory evaluationof wines. At step 4002, wines are selected, purchased, and delivered toa wine panel. At step 4004, wine flights are arranged. At step 4006,labels are printed for wine bottles and glasses. At step 4008, bottlesare labeled. At step 4010, glasses are labeled. At step 4012, a sampleorder by flight sheets, flight pouring sheets, and sample order assemblysheets are generated. At step 4014, data entry is performed; that is,wine metadata is gathered and a replicate wine log are entered. At step4016, Scantron® sheets are assembled.

FIG. 41 depicts an example of multidimensional scaling of results fromwine panelists, according to some embodiments. The multidimensionalscaling of the results in FIG. 41 shows relative panelist (A-H) groupinganalysis based on multi-dimensional scaling of wine characteristic data.In this example, the panelists H, C, D, and B represent a close groupingthat can be used as the basis for the global intensity value for a winedescriptor. The remaining panelists A, E, and G represent outliers whoseintensity values need not be used as the global intensity value for thewine descriptor.

For purposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the description. It will beapparent, however, to one skilled in the art that embodiments of thedisclosure can be practiced without these specific details. In someinstances, modules, structures, processes, features, and devices areshown in block diagram form in order to avoid obscuring the description.In other instances, functional block diagrams and flow diagrams areshown to represent data and logic flows. The components of blockdiagrams and flow diagrams (e.g., modules, blocks, structures, devices,features, etc.) may be variously combined, separated, removed,reordered, and replaced in a manner other than as expressly describedand depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”,“other embodiments”, “one series of embodiments”, “some embodiments”,“various embodiments”, or the like means that a particular feature,design, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of, for example, the phrase “in one embodiment” or “in anembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, whetheror not there is express reference to an “embodiment” or the like,various features are described, which may be variously combined andincluded in some embodiments, but also variously omitted in otherembodiments. Similarly, various features are described that may bepreferences or requirements for some embodiments, but not otherembodiments. Moreover, the word “or” need not be construed using anexclusive OR operator, and may be construed as a non-exclusive OR (e.g.,an “and/or”).

The language used herein has been principally selected for readabilityand instructional purposes, and it may not have been selected todelineate or circumscribe the inventive subject matter. It is thereforeintended that the scope of the invention be limited not by this detaileddescription, but rather by any claims that issue on an application basedhereon. Accordingly, the disclosure of the embodiments of the inventionis intended to be illustrative, but not limiting, of the scope of theinvention, which is set forth in the following claims.

Various embodiments are described herein as examples. It will beapparent to those skilled in the art that various modifications may bemade and other embodiments can be used without departing from thebroader scope of the present invention. Therefore, these and othervariations upon the exemplary embodiments are intended to be covered bythe present invention.

The above-described functions and components may be comprised ofinstructions that are stored on a storage medium such as anon-transitory computer readable medium. The instructions may beretrieved and executed by a processor. Some examples of instructions aresoftware, program code, and firmware. Some examples of storage mediumare memory devices, tape, disks, integrated circuits, and servers. Theinstructions are operational when executed by the processor to directthe processor to operate in accord with some embodiments. Those skilledin the art are familiar with instructions, processor(s), and storagemedium.

1. (canceled)
 2. A system comprising: at least one hardware processor;at least one storage device configured to store a set of wineevaluations from a set of wine panelists, each wine evaluation of theset of wine evaluations generated by a respective wine panelist of theset of wine panelists, each wine evaluation including sets of intensityvalues for a set of wines, each set of intensity values describing a setof wine characteristics for a respective wine of the set of wines, eachintensity value of the set of intensity values describing a respectivewine characteristic of the set of wine characteristics, a particularwine evaluation of the set of wine evaluations generated by a particularwine panelist of the set of wine panelists including a set of particularintensity values describing a particular wine characteristic for the setof wines, and store sets of primary global intensity values for the setof wines, the sets of primary global intensity values generated from theset of wine evaluations from the set of wine panelists, each set ofprimary global intensity values describing the set of winecharacteristics for a respective wine of the set of wines, the sets ofprimary global intensity values including a set of particular primaryglobal intensity values describing the particular wine characteristicacross the set of wines; a bias management module configured to beexecuted by the at least one hardware processor to compare eachparticular intensity value of a group of particular intensity valuesgenerated by the particular wine panelist and describing the particularwine characteristic for each wine of a group of wines of the set ofwines against a corresponding particular primary global intensity valueof a group of particular primary global intensity values describing theparticular wine characteristic for each wine of the group of wines todetermine a group of accuracy deviations, and evaluate each accuracydeviation of the group of accuracy deviations to determine whether theparticular wine panelist is substantially consistently inaccurate by astatistically significant amount across a significant percentage of thegroup of wines indicative of a relative bias of the particular winepanelist for the particular wine characteristic for the group of wines,and if so determine a measure indicative of an amount of the relativebias of the particular wine panelist for the particular winecharacteristic for the group of wines; and a processing moduleconfigured to be executed by the at least one hardware processor to ifthe bias management module determines there is a relative bias of theparticular wine panelist for the particular wine characteristic for thegroup of wines and determines the measure indicative of the amount ofrelative bias for the particular wine characteristic for the group ofwines, modify the group of particular intensity values based on themeasure, and generate a group of updated global intensity values basedon the group of modified particular intensity values of the particularwine panelist for the particular wine characteristic for the group ofwines to replace the group of particular primary global intensityvalues.
 3. The system of claim 2, wherein each wine evaluation of theset of wine evaluations is received from the respective wine panelist inresponse to a blind taste test.
 4. The system of claim 2, wherein eachof the primary global intensity values and each of the updated globalintensity values is generated based on an average of the set ofintensity values of the set of wine panelists for the respective winecharacteristic for the set of wines.
 5. The system of claim 2, whereinthe statistically significant amount defines a predetermined number ofone or more standard deviations.
 6. The system of claim 2, wherein thesignificant percentage is at least 50 percent.
 7. The system of claim 2,further comprising: an engine configured to use the primary globalintensity values or updated global intensity values to assist a user inselecting a particular wine; and an interface configured to present arecommendation for the particular wine to the user.
 8. The system ofclaim 2, further comprising: an accuracy management module configured tobe executed by the at least one hardware processor to evaluate theaccuracy deviations to determine a second measure representing how manyparticular intensity values of the set of intensity values for theparticular wine characteristic for the particular wine panelist for thegroup of wines are deemed inaccurate; wherein the processing module isfurther configured to be executed by the at least one hardware processorto if the bias management module does not determine there is a relativebias of the particular wine panelist for the particular winecharacteristic for the group of wines, and if the accuracy managementmodule determines that the second measure indicates the group ofparticular intensity values generated by the particular wine panelistand describing the particular wine characteristic for the group of thewines cannot be trusted, generate the group of updated global intensityvalues describing the particular wine characteristic for the group ofwines to replace the group of particular primary global intensity valuesfor the particular wine characteristic for the particular wine byexcluding the group of particular intensity values generated by theparticular wine panelist and describing the particular winecharacteristic for the group of wines regardless of whether eachparticular intensity value of the group of particular intensity valuesis deemed inaccurate; and if the bias management module does notdetermine there is a relative bias of the particular wine panelist forthe particular wine characteristic for the group of wines, and if theaccuracy management module does not determine that the second measureindicates the group of particular intensity values generated by theparticular wine panelist and describing the particular winecharacteristic for the group of wines cannot be trusted, generate one ormore particular updated global intensity values of the group of updatedglobal intensity values generated by the particular wine panelist anddescribing the particular wine characteristic for each wine of the groupof wines to replace one or more particular primary global intensityvalues of the group of particular primary global intensity values byexcluding the one or more particular intensity values generated by theparticular wine panelist that the accuracy management module deemedinaccurate.
 9. A method comprising: storing a set of wine evaluationsfrom a set of wine panelists, each wine evaluation of the set of wineevaluations generated by a respective wine panelist of the set of winepanelists, each wine evaluation including sets of intensity values for aset of wines, each set of intensity values describing a set of winecharacteristics for a respective wine of the set of wines, eachintensity value of the set of intensity values describing a respectivewine characteristic of the set of wine characteristics, a particularwine evaluation of the set of wine evaluations generated by a particularwine panelist of the set of wine panelists including a set of particularintensity values describing a particular wine characteristic for the setof wines; storing sets of primary global intensity values for the set ofwines, the sets of primary global intensity values generated from theset of wine evaluations from the set of wine panelists, each set ofprimary global intensity values describing the set of winecharacteristics for the respective wine of the set of wines, each set ofprimary global intensity values including a set of particular primaryglobal intensity values describing the particular wine characteristicfor the set of wines; comparing each particular intensity value of agroup of particular intensity values generated by the particular winepanelist and describing the particular wine characteristic for each wineof a group of wines of the set of wines against a correspondingparticular primary global intensity value of a group of particularprimary global intensity values describing the particular winecharacteristic for each wine of the group of wines to determine a groupof accuracy deviations; evaluating each accuracy deviation of the groupof accuracy deviations to determine whether the particular wine panelistis substantially consistently inaccurate by a statistically significantamount across a significant percentage of the group of wines indicativeof a relative bias of the particular wine panelist for the particularwine characteristic for the group of wines, and if so determining ameasure indicative of an amount of the relative bias of the particularwine panelist for the particular wine characteristic for the group ofwines; and if it is determined there is a relative bias of theparticular wine panelist for the particular wine characteristic for thegroup of wines and determines the measure indicative of the amount ofrelative bias for the particular wine characteristic for the group ofwines, modifying the group of particular intensity values based on themeasure, and generating a group of updated global intensity values basedon the group of modified particular intensity values of the particularwine panelist for the particular wine characteristic for the group ofwines to replace the group of particular primary global intensityvalues.
 10. The method of claim 9, wherein each wine evaluation of theset of wine evaluations is received from the respective wine panelist inresponse to a blind taste test.
 11. The method of claim 9, wherein eachof the primary global intensity values and each of the updated globalintensity values is generated based on an average of the set ofintensity values of the set of wine panelists for the respective winecharacteristic for the set of wines.
 12. The method of claim 9, whereinthe statistically significant amount defines a predetermined number ofone or more standard deviations.
 13. The method of claim 9, wherein thesignificant percentage is at least 50 percent.
 14. The method of claim9, further comprising: using the primary global intensity values orupdated global intensity values to assist a user in selecting aparticular wine; and presenting a recommendation for the particular wineto the user.
 15. The method of claim 9, further comprising: evaluatingthe accuracy deviations to determine a second measure representing howmany particular intensity values of the set of intensity values for theparticular wine characteristic for the particular wine panelist for thegroup of wines are deemed inaccurate; if it is not determined there is arelative bias of the particular wine panelist for the particular winecharacteristic for the group of wines, and if it is determined that thesecond measure indicates the group of particular intensity valuesgenerated by the particular wine panelist and describing the particularwine characteristic for the group of the wines cannot be trusted,generating the group of updated global intensity values describing theparticular wine characteristic for the group of wines to replace thegroup of particular primary global intensity values for the particularwine characteristic for the particular wine by excluding the group ofparticular intensity values generated by the particular wine panelistand describing the particular wine characteristic for the group of winesregardless of whether each particular intensity value of the group ofparticular intensity values is deemed inaccurate; and if it is notdetermined there is a relative bias of the particular wine panelist forthe particular wine characteristic for the group of wines, and if it isnot determined that the second measure indicates the group of particularintensity values generated by the particular wine panelist anddescribing the particular wine characteristic for the group of winescannot be trusted, generating one or more particular updated globalintensity values of the group of updated global intensity valuesgenerated by the particular wine panelist and describing the particularwine characteristic for each wine of the group of wines to replace oneor more particular primary global intensity values of the group ofparticular primary global intensity values by excluding the one or moreparticular intensity values generated by the particular wine panelistthat were deemed inaccurate.